Statistical Methods and Applications - Metodi statistici e applicazioni

Questo insegnamento può essere scelto dallo studente all'interno dei corsi della Sapienza, purché coerente con il percorso formativo.

Questo insegnamento può essere scelto dallo studente all'interno dei corsi della Sapienza, purché coerente con il percorso formativo.

Questo insegnamento può essere scelto dallo studente all'interno dei corsi della Sapienza, purché coerente con il percorso formativo.

General objectives
The primary objective is to study algorithms and data structures that efficiently solve problems whose solution, using trivial approaches, would require very high resources. The algorithms studied will be implemented in Java language.
Specific objectives
Knowledge and ability to understand Basic data structures and their use in solving sorting, searching, and graph problems will be shown. It will be shown how the data structures described are made available in Java language. The student will be able to determine the computational complexity of algorithms, and associate them with the appropriate complexity class.
Ability to apply knowledge and understanding At the end of the course the students will be able to determine algorithms to efficiently solve complex problems, in particular problems on graphs, and choose the most suitable data structures to obtain an efficient implementation of the algorithm. They will also be able to use the Java classes that implement the data structures studied.
Autonomy of judgment Students will be able to distinguish the computational complexity of problems and algorithms, and to identify the computationally more costly steps in solving a problem.
Communication skills Students will be able to describe, in appropriate terms, the characteristics of the main data structures, and identify the primitives needed to efficiently implement an algorithm.
Learning ability Students who pass the exam will be able to take advanced courses, of an applicative nature, that require the use of sophisticated algorithms. They will also be able to appreciate software engineering and computational complexity theory teachings.

The course covers algorithmic programming techniques. The primary goal is to study algorithms and data structures that efficiently solve problems that would require very high computational resources (time or space) using trivial approaches. We will address problems frequently arising in practical applications and real-world situations, such as sorting, searching, and graph traversal, as well as general design techniques including divide and conquer, greedy algorithms, and dynamic programming.

The course will cover the entire algorithm engineering cycle, addressing the design, theoretical analysis, implementation, and experimental evaluation of algorithms and data structures. The students will practice solving computational problems, designing new algorithms, and implementing efficient solutions in Python.

The exam will consist of two tests,

theory test (closed-ended and open-ended questions)

programming test (computer programming exercises), on paper.

To complete the exam it will be necessary to obtain in both a

grade not less than 18. The final grade of the exam will be an average

arithmetic of the two tests' grades.

A colloquium may be required, depending on tests' scores

Introduction to the Design and Analysis of Algorithms

Anany Levitin, Pearson

A more comprehensive and deep text is:

Introduction to Algorithms by T. Cormen, C. Leiserson, R. Rivest, and C. Stein, McGraw-Hill.

Learning goals The primary learning goal of this course is that of exposing students to the body of econometric techniques that are customised to economics applications. The aim of the course is to review this body of techniques, to demonstrate their use in hands-on style, drawing on as wide a range of example as possible, and to interpret each set of results in ways that are most useful to read and represent economic phenomena.
Knowledge and understanding. The course is supposed to broaden students' knowledge of the various econometric techniques that appear in the economics literature, their properties and the way these are applied to data in order to verify economic theory.
Applying knowledge and understanding. Upon successful completion of the course, students will be able to carry out a wide range of tasks in empirical economics, such as recognising the most suitable approaches to analyse the data at hand in order to capture and model its regularities, and intelligibly convey its messages to both economists and broader audiences.
Making judgements. The course develops in a way to spurs students on researching empirical evidence of competing economic theories by respecting the nature of convenient data.
Communication skills. Through study and hands-on sessions, students will acquire the terminology characterising the discipline, which they are required to use in both written and oral dissemination.
Learning skills. Students who complete the course successfully will be acquainted with a method of analysis enabling them to endeavour the main economic issues from an empirical point of view.

APPLIED ECONOMETRICS program:

Introduction to the potential outcome approach.

Neyman's mode of inference.

Outcome regression.

Propensity score.

Propensity score weighting.

Doubly robust estimation.

Instrumental variables.

Causal methods for panel data: difference-in-differences.

Regression discontinuity designs.

Non saranno adottati testi in italiano.

Learning goals Prepare students to proposing solutions to real statistical problems in many research areas.
Knowledge and understanding At the end of the course the students have the ability to understand and solve real practical statistical problems and to propose adequate solutions.
Applying knowledge and understanding Students are required to apply theoretical and computational skills to real problems and case-studies in a wide range of domains.
Making judgements One of the main goals of practical activities is to develop the ability to understand problems and to propose and compare alternative statistical approaches to solve them.
Communication skills Students acquire the ability of discussing problems and of presenting oral and written reports of their practical analyses.
Learning skills The students acquire a series of skills useful for future academic and professional activities.

Questo è il programma della serie di seminari della FAO per l'anno accademico 2024-2025. I seminari si terranno alle 18:00 nell'Aula 34 (Dipartimento di Scienze Statistiche, edificio CU002 4° piano) per 2 ore in modalità blended (è consentita sia la partecipazione in presenza che online).

Collegamento Zoom: https://uniroma1.zoom.us/j/98572638271?pwd=YlhxZExsaUZXcjNkb0hGWnlnbjBmUT09

• February 25th, 2025 – Introduction to the 2025 Seminar Series on Food and Agricultural Statistics, Valerie Bizier, Statistics Division and Ashley Steel, Forest Division
• March 4th, 2025 - Methodology for the disaggregation of SDG indicators, Aida Khalil, Stefano Di Candia, Statistics Division
• March 11th, 2025 - Laboratory: Small Area Estimation techniques for the disaggregation of SDG indicators, Aida Khalil, Statistics Division
• March 18th, 2025 - SDG indicator 15.4.2: Monitoring progress on the conservation of mountain ecosystems – Javier De Lamo, Forestry Division
• March 25th, 2025 - National Forest Monitoring - Javier Garcia Perez, Forestry Division.
• April 1st, 2025 - Laboratory: Monte Carlo techniques for the study of forest cover statistics - Javier Garcia Perez, Forestry Division
• April 8th, 2025 - Wood fuel statistics – Ashley Steel, Forestry Division
• April 15th, 2025 - Laboratory: how to measure Wood Fuel – Oliver Stoner, Forestry Division
• April 29th, 2025 - Food insecurity Experience Scale - Carlo Cafiero, Statistics Division
• May 6th, 2025 - Laboratory: how to measure the Food insecurity Experience Scale - Sara Viviani, Statistics Division
• May 13th, 2025 - Estimating the Prevalence of Undernourishment and the Percentage of people who cannot afford a healthy diet. Theoretical and empirical aspects – Carlo Cafiero, Statistics Division
• May 20th, 2025 - Developing Models with data - Rishi Sharma, Fisheries and Aquaculture Division
• May 27th, 2025 - Understanding SDG 14: from theory to practice - Rishi Sharma, Fisheries and Aquaculture Division

Lavoro di gruppo di alcuni studenti. Gli studenti sono tenuti a sviluppare e risolvere un dato problema come spiegato in classe. Tutto il materiale necessario per sostenere l'esame (idoneità) è disponibile su Moodle. Inoltre, è necessario sviluppare un caso di studio (ricerca breve) scegliendo, a discrezione del gruppo di studenti, un argomento tra quelli presentati dal Team di specialisti FAO.

Materiale didattico rilasciato dai titolari del seminario.

Materiale didattico rilasciato dai titolari del seminario.

Learning goals Knowledge at an intermediate and advanced level of the main issues in official statistics with special attention to data quality
Knowledge and understanding Knowledge and understanding of statistical methods within the topics of official statistics in an changing environment
Applying knowledge and understanding Ability to apply statistical methods for official statistics problems with emphasis on the data quality process
Making judgements Ability of choosing appropriate methods in different problems in official statistics with emphasis on the data quality process
Communication skills Ability of communicating results of the analyses in official statistics with emphasis on the data quality process
Learning skills Students acquire skills useful to approach more advanced topics in official statistics and data quality management

Parte 1: Modifica e Imputazione dei Dati Statistici

• Errori Sistematici
• Modifica Automatica
• Dati Mancanti
• Imputazione
• Modifica Selettiva

Parte 2: Collegamento dei Record e Statistical Matching

• Approccio di Fellegi-Sunter
• Macro Parametrico con CIA
• Micro Parametrico con CIA
• Macro Non Parametrico con CIA
• Micro Non Parametrico con CIA
• Macro Parametrico senza CIA
• Macro Non Parametrico senza CIA

Durante la prova orale, il candidato viene valutato sulla sua conoscenza dei contenuti visti a lezione. Questo può coinvolgere la capacità di rispondere a domande specifiche, di collegare concetti tra loro e di applicare le conoscenze acquisite a situazioni pratiche o problemi. La capacità di rispondere in modo appropriato, coerente e preciso alle domande è un elemento chiave nella valutazione complessiva.

Il materiale principale del corso è distribuito dal docente. Testi utili per approfondimento sono:

• "Data Quality and Record Linkage Techniques" Thomas N. Herzog, Fritz J. Scheuren e William E. Winkler
• "Statistical matching: Theory and practice" D'Orazio, Marcello, Marco Di Zio e Mauro Scanu.

Consultare sito Moodle del corso per le diapositive https://elearning.uniroma1.it/course/view.php?id=15153

Versione Inglese

General goals

This module complements the course on “development finance”, for an overall training on issues pertaining to the economics and financial aspects of human development. The course is based on a general overview of development economics and on a series of case studies.

Knowledge and understanding

Knowledge of the basic concepts and of the main theories elaborated in the field. Historical perspective and awareness of the existence of different interpretative positions.

Applying knowledge and understanding

At the end of the course the students will be able to formalize development problems and to recur to theoretical and empirical methods to try to solve them.

Making judgements

The students will develop critical skills through the analysis of several case studies over a wide range of economic models, which are affected by different theoretical approaches.

Communication skills

The students will acquire the technical-scientific language of the discipline, which must be appropriately used both in written and oral exams.

Learning skills

The students who pass the exam will have learned a method of analysis that allows them to tackle the study of more complex issues in development from the perspective of an economist.

Al termine del corso, gli studenti saranno in grado di comprendere l’architettura globale della finanza per lo sviluppo e il funzionamento e le operazioni dei singoli attori che ne fanno parte. Il corso mira esplicitamente a fornire una formazione pratica, con legami scientifici e professionali con il mercato del lavoro nel campo della finanza per lo sviluppo.

Il corso si fonda esplicitamente sul principio del pluralismo metodologico e teorico. Gli studenti saranno introdotti alle teorie alternative sul funzionamento delle iniziative pubbliche e private nel campo della finanza per lo sviluppo e saranno in grado di comprendere il dibattito attuale in materia, nonché i principali sviluppi contemporanei a livello globale.

The course discusses how the financial system and financial dynamics can help or hinder the process of human development. To this aim, it first introduces the students to the notion(s) of development and the social role(s) of money and finance, and it then discusses how financial actors and practices might have an impact on the real economy, on the environment, and on social and political dynamics. During the course, a substantial number of lecture hours are devoted to seminars from practictioners and researchers in the field of development finance.

Syllabus:

• A brief summary of international monetary economics and international finance
• Flows of funds representation of the economy, stock-flow consistency in an open economy; the balance of payments; the international investment position. Public, private, and foreign debt.
• The international monetary system; regional and global institutions; the role of the dollar.
• Open-economy macroeconomics; economic policy in an open economy; the trilemma of monetary economics, and Rodrik’s globalization trilemma.
• Concepts of development
• A short history of economic thought on development;
• Contemporary economic theories of growth and structural change;
• Multidimensional development: measurement issues and the Sustainable Development Goals:
• “Shared prosperity”: growth, poverty, and inequality;
• Civic and political development: rule of law, sovereignty, democracy, fairness and justice.
• Environmental sustainability: resource depletion, loss of biodiversity, climate change.
• Fundamentals of financial economics
• The social roles of the financial system: allocative, monetary, and risk management;
• Efficiency of the real and financial markets: allocative and informative efficency;
• Institutions, instruments, and actors of the global financial system. The global architecture of finance for development.
• Finance for development
• The real effect of financial developments;
• The private and public financing of development goals;
• Financial and economic crises, and the obstacles to and difficulties of development.

Modulo: DEVELOPMENT ECONOMICS
N/D
Modulo: DEVELOPMENT FINANCE
N/D

For students who attend all classes, the exam consists of two projects based on the application to real world data of the theories learnt in class.

For students who do not attend all classes, the final exam consists of a written and an oral examination. The written test consists of 3-4 open-ended questions, of which one or more may be in the form of exercises. The subsequent oral examination consists of a discussion of the written test and of one or more further topics.

Ocampo J.A., Kregel J., Griffith-Jones S. (2007), International Finance and Development, New York: United Nations.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Spratt S. (2011), Development Finance, Abingdon: Routledge.

Ocampo J.A., Kregel J., Griffith-Jones S. (2007), International Finance and Development, New York: United Nations.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Spratt S. (2011), Development Finance, Abingdon: Routledge.

General goals This module complements the course on “development finance”, for an overall training on issues pertaining to the economics and financial aspects of human development. The course is based on a general overview of development economics and on a series of case studies.
Knowledge and understanding Knowledge of the basic concepts and of the main theories elaborated in the field. Historical perspective and awareness of the existence of different interpretative positions.
Applying knowledge and understanding At the end of the course the students will be able to formalize development problems and to recur to theoretical and empirical methods to try to solve them.
Making judgements The students will develop critical skills through the analysis of several case studies over a wide range of economic models, which are affected by different theoretical approaches.
Communication skills The students will acquire the technical-scientific language of the discipline, which must be appropriately used both in written and oral exams.
Learning skills
The students who pass the exam will have learned a method of analysis that allows them to tackle the study of more complex issues in development from the perspective of an economist.

DEVELOPMENT ECONOMICS
The course discusses how the financial system and financial dynamics can help or hinder the process of human development. To this aim, it first introduces the students to the notion(s) of development and the social role(s) of money and finance, and it then discusses how financial actors and practices might have an impact on the real economy, on the environment, and on social and political dynamics. During the course, a substantial number of lecture hours are devoted to seminars from practictioners and researchers in the field of development finance.

Syllabus:

• A brief summary of international monetary economics and international finance
• Flows of funds representation of the economy, stock-flow consistency in an open economy; the balance of payments; the international investment position. Public, private, and foreign debt.
• The international monetary system; regional and global institutions; the role of the dollar.
• Open-economy macroeconomics; economic policy in an open economy; the trilemma of monetary economics, and Rodrik’s globalization trilemma.
• Concepts of development
• A short history of economic thought on development;
• Contemporary economic theories of growth and structural change;
• Multidimensional development: measurement issues and the Sustainable Development Goals:
• “Shared prosperity”: growth, poverty, and inequality;
• Civic and political development: rule of law, sovereignty, democracy, fairness and justice.
• Environmental sustainability: resource depletion, loss of biodiversity, climate change.
• Fundamentals of financial economics
• The social roles of the financial system: allocative, monetary, and risk management;
• Efficiency of the real and financial markets: allocative and informative efficency;
• Institutions, instruments, and actors of the global financial system. The global architecture of finance for development.
• Finance for development
• The real effect of financial developments;
• The private and public financing of development goals;
• Financial and economic crises, and the obstacles to and difficulties of development.

Modulo: DEVELOPMENT ECONOMICS
N/D
Modulo: DEVELOPMENT FINANCE
N/D

DEVELOPMENT FINANCE
The course discusses how the financial system and financial dynamics can help or hinder the process of human development. To this aim, it first introduces the students to the notion(s) of development and the social role(s) of money and finance, and it then discusses how financial actors and practices might have an impact on the real economy, on the environment, and on social and political dynamics. During the course, a substantial number of lecture hours are devoted to seminars from practictioners and researchers in the field of development finance.

Syllabus:

• A brief summary of international monetary economics and international finance
• Flows of funds representation of the economy, stock-flow consistency in an open economy; the balance of payments; the international investment position. Public, private, and foreign debt.
• The international monetary system; regional and global institutions; the role of the dollar.
• Open-economy macroeconomics; economic policy in an open economy; the trilemma of monetary economics, and Rodrik’s globalization trilemma.
• Concepts of development
• A short history of economic thought on development;
• Contemporary economic theories of growth and structural change;
• Multidimensional development: measurement issues and the Sustainable Development Goals:
• “Shared prosperity”: growth, poverty, and inequality;
• Civic and political development: rule of law, sovereignty, democracy, fairness and justice.
• Environmental sustainability: resource depletion, loss of biodiversity, climate change.
• Fundamentals of financial economics
• The social roles of the financial system: allocative, monetary, and risk management;
• Efficiency of the real and financial markets: allocative and informative efficency;
• Institutions, instruments, and actors of the global financial system. The global architecture of finance for development.
• Finance for development
• The real effect of financial developments;
• The private and public financing of development goals;
• Financial and economic crises, and the obstacles to and difficulties of development.

Modulo: DEVELOPMENT ECONOMICS
N/D
Modulo: DEVELOPMENT FINANCE
N/D

DEVELOPMENT ECONOMICS
For students who attend all classes, the exam consists of two projects based on the application to real world data of the theories learnt in class.

For students who do not attend all classes, the final exam consists of a written and an oral examination. The written test consists of 3-4 open-ended questions, of which one or more may be in the form of exercises. The subsequent oral examination consists of a discussion of the written test and of one or more further topics.

DEVELOPMENT FINANCE
For students who attend all classes, the exam consists of two projects based on the application to real world data of the theories learnt in class.

For students who do not attend all classes, the final exam consists of a written and an oral examination. The written test consists of 3-4 open-ended questions, of which one or more may be in the form of exercises. The subsequent oral examination consists of a discussion of the written test and of one or more further topics.

DEVELOPMENT ECONOMICS
Ocampo J.A., Kregel J., Griffith-Jones S. (2007), International Finance and Development, New York: United Nations.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Spratt S. (2011), Development Finance, Abingdon: Routledge.

Ocampo J.A., Kregel J., Griffith-Jones S. (2007), International Finance and Development, New York: United Nations.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Spratt S. (2011), Development Finance, Abingdon: Routledge.

DEVELOPMENT FINANCE
Ocampo J.A., Kregel J., Griffith-Jones S. (2007), International Finance and Development, New York: United Nations.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Spratt S. (2011), Development Finance, Abingdon: Routledge.

Ocampo J.A., Kregel J., Griffith-Jones S. (2007), International Finance and Development, New York: United Nations.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Spratt S. (2011), Development Finance, Abingdon: Routledge.

Versione Inglese

General goals

This module complements the course on “development finance”, for an overall training on issues pertaining to the economics and financial aspects of human development. The course is based on a general overview of development economics and on a series of case studies.

Knowledge and understanding

Knowledge of the basic concepts and of the main theories elaborated in the field. Historical perspective and awareness of the existence of different interpretative positions.

Applying knowledge and understanding

At the end of the course the students will be able to formalize development problems and to recur to theoretical and empirical methods to try to solve them.

Making judgements

The students will develop critical skills through the analysis of several case studies over a wide range of economic models, which are affected by different theoretical approaches.

Communication skills

The students will acquire the technical-scientific language of the discipline, which must be appropriately used both in written and oral exams.

Learning skills

The students who pass the exam will have learned a method of analysis that allows them to tackle the study of more complex issues in development from the perspective of an economist.

Al termine del corso, gli studenti saranno in grado di comprendere l’architettura globale della finanza per lo sviluppo e il funzionamento e le operazioni dei singoli attori che ne fanno parte. Il corso mira esplicitamente a fornire una formazione pratica, con legami scientifici e professionali con il mercato del lavoro nel campo della finanza per lo sviluppo.

Il corso si fonda esplicitamente sul principio del pluralismo metodologico e teorico. Gli studenti saranno introdotti alle teorie alternative sul funzionamento delle iniziative pubbliche e private nel campo della finanza per lo sviluppo e saranno in grado di comprendere il dibattito attuale in materia, nonché i principali sviluppi contemporanei a livello globale.

The course discusses how the financial system and financial dynamics can help or hinder the process of human development. To this aim, it first introduces the students to the notion(s) of development and the social role(s) of money and finance, and it then discusses how financial actors and practices might have an impact on the real economy, on the environment, and on social and political dynamics. During the course, a substantial number of lecture hours are devoted to seminars from practictioners and researchers in the field of development finance.

Syllabus:

• A brief summary of international monetary economics and international finance
• Flows of funds representation of the economy, stock-flow consistency in an open economy; the balance of payments; the international investment position. Public, private, and foreign debt.
• The international monetary system; regional and global institutions; the role of the dollar.
• Open-economy macroeconomics; economic policy in an open economy; the trilemma of monetary economics, and Rodrik’s globalization trilemma.
• Concepts of development
• A short history of economic thought on development;
• Contemporary economic theories of growth and structural change;
• Multidimensional development: measurement issues and the Sustainable Development Goals:
• “Shared prosperity”: growth, poverty, and inequality;
• Civic and political development: rule of law, sovereignty, democracy, fairness and justice.
• Environmental sustainability: resource depletion, loss of biodiversity, climate change.
• Fundamentals of financial economics
• The social roles of the financial system: allocative, monetary, and risk management;
• Efficiency of the real and financial markets: allocative and informative efficency;
• Institutions, instruments, and actors of the global financial system. The global architecture of finance for development.
• Finance for development
• The real effect of financial developments;
• The private and public financing of development goals;
• Financial and economic crises, and the obstacles to and difficulties of development.

Modulo: DEVELOPMENT ECONOMICS
N/D
Modulo: DEVELOPMENT FINANCE
N/D

For students who attend all classes, the exam consists of two projects based on the application to real world data of the theories learnt in class.

For students who do not attend all classes, the final exam consists of a written and an oral examination. The written test consists of 3-4 open-ended questions, of which one or more may be in the form of exercises. The subsequent oral examination consists of a discussion of the written test and of one or more further topics.

Ocampo J.A., Kregel J., Griffith-Jones S. (2007), International Finance and Development, New York: United Nations.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Spratt S. (2011), Development Finance, Abingdon: Routledge.

Ocampo J.A., Kregel J., Griffith-Jones S. (2007), International Finance and Development, New York: United Nations.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Spratt S. (2011), Development Finance, Abingdon: Routledge.

Learning goals. Aim of the course is to allow students to broaden their knowledge of economics, sociology, and other social sciences in an interdisciplinary way.
Knowledge and understanding. Historical perspective and awareness of the existence of different interpretative positions in the context of social sciences.
Applying knowledge and understanding. At the end of the course students will be able to deal with different models in a critical way.
Making judgements. Students will develop critical skills through different theoretical approaches.
Communication skills. Students, through the study, acquire the language of different disciplines, which must be appropriately used both in written and oral exams.
Learning skills. Students who pass the exam have learned a method of analysis that allows them to tackle the study of more complex models.

Corso tenuto in inglese.

List of readings to be agreed with students attending the course.

Letture in classe e discussioni collettive

Letture variabili di anno in anno.

Letture variabili di anno in anno.

Learning goals The primary learning goal of this course is that of exposing students to the body of econometric techniques that are customised to economics applications. The aim of the course is to review this body of techniques, to demonstrate their use in hands-on style, drawing on as wide a range of example as possible, and to interpret each set of results in ways that are most useful to read and represent economic phenomena.
Knowledge and understanding. The course is supposed to broaden students' knowledge of the various econometric techniques that appear in the economics literature, their properties and the way these are applied to data in order to verify economic theory.
Applying knowledge and understanding. Upon successful completion of the course, students will be able to carry out a wide range of tasks in empirical economics, such as recognising the most suitable approaches to analyse the data at hand in order to capture and model its regularities, and intelligibly convey its messages to both economists and broader audiences.
Making judgements. The course develops in a way to spurs students on researching empirical evidence of competing economic theories by respecting the nature of convenient data.
Communication skills. Through study and hands-on sessions, students will acquire the terminology characterising the discipline, which they are required to use in both written and oral dissemination.
Learning skills. Students who complete the course successfully will be acquainted with a method of analysis enabling them to endeavour the main economic issues from an empirical point of view.

Il corso è incentrato sull’analisi di casi di studio reali che affrontano temi economici e sociali rilevanti, come la discriminazione nella concessione dei mutui, le determinanti del reddito individuale, il consumo di sigarette e le spese per beni durevoli. Ogni caso rappresenta l’occasione per applicare le tecniche econometriche più appropriate (come i modelli per variabili binarie e ordinate, l’analisi di dati troncati e censurati e i metodi Monte Carlo) che vengono utilizzate come strumenti per l’analisi e l’interpretazione dei dati.

• Introduzione al corso. Il metodo della Massima Verosimiglianza.
• Caso studio: la discriminazione nella concessione dei mutui. Come trattare variabili dipendenti binarie con i modelli probit e logit. Discriminazione di genere nella concessione dei mutui in Italia.
• Caso studio: le determinanti del reddito individuale. La regressione per dati in intervalli e sue applicazioni.
• Caso studio: il consumo di sigarette. Il modello probit per dati ordinabili con eccesso di zeri.
• Caso studio: il “New Jersey negative income tax experiment”. Dati troncati.
• Caso studio: le spese per beni durevoli. Dati censurati.
• I metodi dell'indagine campionaria ed analisi di dati campionari.
• Scelte in condizioni di rischio: stime di modelli strutturali e modelli mistura finiti.
• I metodi Monte Carlo.

Lo studente deve analizzare un data set, utilizzando le tecniche discusse durante il corso, discuterne i risultati. L'esame si prefigge di valutare l'abilità dello studente nell'affrontare problemi empirici di natura economica, come il riconoscere gli approcci più appropriati per analizzare i dati disponibili in modo da catturarne le regolarità, e comunicarne i messaggi in maniera intelligibile.

Train, K., 2009, Discrete Choice Methods with Simulation, Cambridge University Press

Verbeek, M., 2017, A Guide to Modern Econometrics, 5th Edition, Hoboken, NJ: John Wiley and Sons

Lecture notes and exercises are made available by the instructor via the moodle website. This is also valid for non-attendees.

Mark N. Harris, Xueyan Zhao, A zero-inflated ordered probit model, with an application to modelling tobacco consumption, Journal of Econometrics,

Volume 141, Issue 2, 2007, Pages 1073-1099.

Hausman, Jerry and Wise, David. (2012). The Evaluation of Results from Truncated Samples: The New Jersey Income Maintenance Experiment. NBER Book Chapters. 5.

Obiettivi generali Obiettivo del corso è l'introduzione ai sistemi di supercalcolo HPC, alla loro architettura ed ai loro principi di funzionamento. Obiettivo del corso è poi l'introduzione alla programmazione parallela e distribuita, con lo scopo di abbattere i tempi di risoluzione di problemi particolarmente complessi mediante l'impiego coordinato di numerose unità di calcolo.
Obiettivi specifici a) Conoscenza e capacità di comprensione Gli studenti apprenderanno quali sono i principi su cui si basa un sistema HPC e come organizzare la strategia risolutiva di un algoritmo che possa avvantaggiarsi della presenza di più unità di calcolo.
b) Capacità di applicare conoscenza e comprensione Al termine del corso gli studenti saranno in grado di realizzare semplici applicazioni parallele e distribuite, in grado di sfruttare l'aumentata capacità di calcolo di un sistema HPC. Gli studenti saranno inoltre in grado di eseguire concretamente gli algoritmi sviluppati adoperando una infrastruttura di calcolo realmente esistente.
c) Autonomia di giudizio Gli studenti svilupperanno la capacità di individuare quelle particolari tipologie di problemi per le quali l'utilizzo di un approccio parallelo o distribuito è di significativo aiuto.
d) Abilità comunicativa Gli studenti, attraverso lo studio e lo svolgimento di esercizi pratici, acquisiscono il linguaggio tecnico-scientifico della disciplina.
e) Capacità di apprendimento Gli studenti che supereranno l’esame avranno appreso i paradigmi da utilizzare per applicare tecniche di calcolo parallelo e distribuito alla risoluzione di problemi complessi, sfruttando le capacità di calcolo di un sistema HPC.

1. Introduzione ai sistemi HPC

⁃ Definizione introduttive di un sistema HPC

⁃ Architetture di un sistema HPC

⁃ Scalabilità, prestazioni e bilanciamento del carico nei sistemi HPC

2. Introduzione alla shell Linux

⁃ Il sistema operativo Linux

⁃ I comandi di base

⁃ shell scripting

3. Programmazione parallela

⁃ Principi di programmazione parallela

⁃ Multithreading e multiprocessing

⁃ Programmazione parallela in Python

⁃ Esempi di applicazioni parallele Python

4. Programmazione distribuita

⁃ Principi di programmazione distribuita

⁃ OpenMPI

⁃ Programmazione distribuita in Python

⁃ Esempi di applicazioni distribuite Python

• Ottimizzazione delle prestazioni
• Strumenti di profilazione, debug e testing per applicazioni HPC
• Casi di studio

L'esame finale consiste in una prova di laboratorio durante la quale gli studenti saranno chiamati ad ideare e sviluppare un algoritmo distribuito o parallelo per la risoluzione di un problema dato, utilizzando l'infrastruttura di HPC in dotazione al Dipartimento di Scienze Statistiche.

Appunti, articoli scientifici ed altro materiale didattico forniti dal docente durante il corso.

Appunti, articoli scientifici ed altro materiale didattico forniti dal docente durante il corso.

Learning goals Working knowledge of the main models of international economics and international finance.
Knowledge and understanding. Upon successful completion of the course, students will be able to analyse actual economic problems in terms of competing theories and models.
Applying knowledge and understanding. Upon successful completion of the course, students will be able to understand the explicit and implicit hypotheses informing the main economic policy proposals in the current debate.
Making judgements. The course is explicitly based on the principle of methodological and theoretical pluralism. Students will be introduced to at least two competing models for each economic problem considered, and will understand the criteria with which to personally choose their favorite interpretation.
Communication skills. Through study and hands-on sessions, students will become proficient in the jargon and technical language of the discipline, which they must use in both written and oral examinations.
Learning skills. Students that successfully complete the course will have learnt a method of analysis that will allow them to tackle and understand the main economic issues of today, both in subsequent economic courses and in the fruition and participation to the public debate.

Il corso analizza il contesto della “seconda globalizzazione” dal punto di vista istituzionale e del sistema monetario internazionale, nonché le sue principali aree geografiche (in particolare l’Unione Europea). Considera poi le principali teorie della determinazione dei tassi di cambio; i sistemi di tasso di cambio fisso e flessibile; il rapporto tra bilancia dei pagamenti e tasso di cambio; e alcuni concetti di competitività. Analizza infine l’impatto delle politiche fiscali e monetarie, e alcuni modelli di crescita in un’economia aperta. In tutto il corso particolare enfasi è attribuita al ruolo dei movimenti di capitale e ai mercati finanziari.

Il programma completo e costantemente aggiornato è disponibile alla pagina moodle del corso: https://elearning.dss.uniroma1.it/course/view.php?id=26

Valutazione di un progetto svolto durante il semestre; esame scritto e orale.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Lavoie M. (2014), Post-Keynesian Economics: New Foundations, Cheltenham: Edward Elgar, chapter 7.

Salvatore D. (2016), International Economics, New York: John Wiley & Sons, chapters 13-21.

Lavoie M. (2014), Post-Keynesian Economics: New Foundations, Cheltenham: Edward Elgar, chapter 7.

Goals. With this activity (based on internships) students merge their academic knowledge with practical skills. They also develop independent judgement and communication skills.

Il programma dell'attività di tirocinio curriculare è concordato dallo studente e l'ente ospitante, e deve essere approvate dal presidente del corso di studi prima dell'inizio del tirocinio stesso.

• Tirocinio

Per acquisire i crediti formativi gli studenti devo svolgere una relazione in cui viene descritta l'attività svolta e le competenze acquisite durante l'esperienza formativa.

Non vi è un testo di riferimento

Obiettivi generali Obiettivo del corso è la presentazione delle nozioni di base necessarie all’utilizzo di un linguaggio di programmazione imperativo di uso generale. In particolare, si mostrerà l’uso del linguaggio di programmazione Python 3.
Obiettivi specifici a) Conoscenza e capacità di comprensione Gli studenti conosceranno i costrutti di base del linguaggio Python 3, saranno in grado di comprendere un semplice programma scritto in Python 3 e scrivere programmi nello stesso linguaggio. Saranno inoltre in grado di utilizzare un ambiente integrato di sviluppo (IDE).
b) Capacità di applicare conoscenza e comprensione Al termine del corso gli studenti saranno in grado di risolvere semplici problemi di natura algoritmica utilizzando il linguaggio di programmazione Python 3, correggere errori sintattici e semantici utilizzando un ambiente di sviluppo, valutare la correttezza e la complessità delle soluzioni individuate.
c) Autonomia di giudizio Gli studenti svilupperanno la capacità di formalizzare algoritmi utilizzando un linguaggio di programmazione, scegliendo i costrutti più adatti a risolvere il singolo problema. Saranno in grado di valutare, la correttezza, la leggibilità e la generalità delle soluzioni individuate.
d) Abilità comunicativa Gli studenti acquisiranno la capacità di esprimere in maniera formale un procedimento mentale per risolvere un problema, e di comprendere i punti cruciali di un algoritmo.
e) Capacità di apprendimento Gli studenti saranno in grado di apprendere facilmente l’uso di linguaggi di programmazione imperativi, apprezzando analogie e differenze rispetto al linguaggio Python 3.

Il corso introduce la programmazione imperativa tramite il linguaggio Python. La prima parte tratta i costrutti fondamentali del linguaggio: variabili, espressioni, tipi di dati numerici, istruzioni condizionali, iterazione e definizione di funzioni. Vengono approfonditi la ricorsione, i diagrammi dello stack e i limiti della ricorsione. La seconda parte è dedicata alle principali strutture dati: stringhe e manipolazione del testo, liste, dizionari e gestione dei file. Il corso si conclude con i fondamenti della codifica dell'informazione: rappresentazione binaria, decimale ed esadecimale di numeri interi, e standard di codifica dei caratteri (ASCII, Unicode, UTF-8).

• Variabili, espressioni e istruzioni
• Tipi di dato e Stringhe
• Chiamata e definizione di funzioni
• Istruzioni condizionali
• Istruzioni di iterazione
• Iterazione e ricorsione
• Liste, Matrici e sequenze di dati
• Dizionari
• Manipolazione dei file
• Rappresentazione dei dati e encoding

La valutazione avviene tramite una prova scritta in laboratorio informatico. Il candidato deve risolvere esercizi di programmazione in Python, dimostrando la capacità di applicare i costrutti e le strutture dati trattati nel corso. Viene valutata principalmente la correttezza del codice. Altri aspetti sono la scelta appropriata degli strumenti e la chiarezza della soluzione proposta.

Non ci saranno prove intermedie (esoneri).

Il materiale principale del corso è distribuito dal docente tramite il sito Moodle. Testi utili per approfondimento sono:

• Allen Downey, Think Python — How to Think Like a Computer Scientist; disponibile sotto licenza GNU Free Documentation License
• Cay S. Horstmann, Rance D. Necaise — Python for Everyone. Wiley.

Consultare il sito Moodle del corso per gli esercizi: https://elearning.uniroma1.it/course/view.php?id=18876

Learning goals
Students will be introduced to the following topics
1. The Monetary, banking and financial statistics. Why Bank of Italy collects statistics and what collects. Application: recent developments of the banks. 2. The financial accountsThe financial accounts structure.Household wealth after Piketty: an international comparison.The financial structure of the companies. 3. The balance of payments and international investment positionThe Italian balance of payments: the structure and recent developments.The procedure on excessive macroeconomic imbalances in Europe.Funds held abroad by the families. 4. The sample surveys of the Bank of ItalyThe survey on Household Income: recent results and a long-term look.The survey on inflation and growth expectations.

Learning goals. General learning targets: The course is organised as a series of classes where the students will have the possibility to solve and discuss the solutions of a series of advanced exercises on the theory of stochastic processes.
Knowledge and understanding. At the end of the course the students will acquire the ability to solve autonomously simple and more advanced exercises on the theory of stochastic processes.
Applying knowledge and understanding. During the course the students will exercise the ability to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space and to solve simple applied problems in new environments and broader contexts.
Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space.
Communication skills. The students will exercise the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will acquire familiarity with the main ideas that are behind the stochastic model, e.g., the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour. Learning skills. The students will acquire autonomy in studying more advanced theoretical aspects of stochastic processes and in applying the main ideas of stochastic processes to the subsequent studies in the area of statistics and finance.

Random walks (about 6 hours)

Exercises on: Markovianity, temporal and spatial invariance, random walks with reflecting and absorbing barriers, reflection principle, ballot theorem, distribution of the maximum, hitting time theorem, first and second arc sine law, random walks and generating functions.

Brownian motion (about 6 hours)

Exercises on: path properties of Brownian motion, Brownian motion as a strong Markov process, transience and recurrence.

Branching processes (about 2 hours)

Exercises on: expectation and variance of the population size, probability of extinction of the population.

Markov chains (about 6 hours)

Exercises on: transition matrix, classification of states, classification of chains, stationary distribution and limit theorem, chains with finitely many states.

Poisson processes (about 2 hours)

Exercises on the main properties of Poisson processes.

The students are required to prepare a short written essay where they solve some exercises on the topics studied during the course.

In the solution of the exercises the students must show that they have acquired the techniques to solve the problems, the intuition, the technical language, and that they are able to present the topics with all necessary details.

Recommended books:

• G.R. Grimmett and D.R. Stirzaker. One Thousand Exercises in Probability. 3rd edn, OUP, 2001
• P. Mörters and Y. Peres. Brownian Motion. Cambridge Series in Statistical and Probabilistic Mathematics, 2010
• G.R. Grimmett and D.R. Stirzaker. One Thousand Exercises in Probability. 3rd edn, OUP, 2001
• P. Mörters and Y. Peres. Brownian Motion. Cambridge Series in Statistical and Probabilistic Mathematics, 2010
• G.R. Grimmett and D.R. Stirzaker. Probability and Random Processes. 3rd edn, OUP, 2001
• D. Williams. Probability with Martingales. CUP, 1991

L'obiettivo generale di queste attività è consentire agli studenti di fondere le loro conoscenze accademiche con competenze professionali. Affrontando problemi pratici e reali, gli studenti sviluppano capacità di giudizio e di comunicazione. Gli studenti possono concordare con il coordinatore del corso di studio o un suo delegato attività di tipo tirocinio per un totale non inferiore a 72 ore. Possono essere proposti anche hackathon o tipi simili di sfide che richiedono competenze statistiche. Occasionalmente gli studenti possono altrimenti concordare con il coordinatore del corso di studio alcune attività come approfondimenti su tematiche specifiche, talvolta legati ad approfondimenti o estensioni di insegnamenti interni al corso di studio. Talvolta il coordinatore del corso di studio propone moduli didattici tematici appositamente organizzati.

L'obiettivo generale di queste attività è consentire agli studenti di fondere le loro conoscenze accademiche con competenze professionali. Gli studenti possono concordare con il coordinatore del corso di studio o un suo delegato attività di tipo tirocinio per un totale non inferiore a 144 ore. Talvolta il coordinatore del corso di studio propone moduli didattici tematici appositamente organizzati. Affrontando problemi pratici e reali, gli studenti sviluppano capacità di giudizio e di comunicazione.

General Targets: Prior educational teaching concern is the students’ understanding of the main (Economic Statistics Modeling) problems and methods for Panel Data making use of parametric estimation. Here the empirical focus is on individuals represented by Decisional Making Units (DMU). More specifically, these are banks typically involved in the European (and also international) banking system. The course will focus on managerial problems of these firms by studying equations such as cost (mostly) and profit functions which are relevant to asses on the Efficiency of banks. Furthermore, students should know both how to solve analytical problems, in order to apply the appropriate methodology, and to interpret results obtained from empirical applications to actual data. Specific Targets: a) Knowledge and capability in understanding. After attending the course, students know and understand main problems of Panel Data. In particular, the course will account for the logic for building empirical models, related to the underlying economic theory (and the consequent subdivisions in endogenous and exogenous variables), with one or more equations in order to evaluate the degree of efficiency of a typical Decisional Making Unit (here the bank and possibly the insurance company). We will study the main estimation methods of Panel Data for solving efficiency problems pertaining a firm traditionally operating in the private sector. b) Capability of applying knowledge and comprehension At the end of the course students are able to formalize and solve problems by means of specific methods as well as treating fundamental models of Panel Data to answer questions on the Efficiency and Productivity Analysis for the banking system. Finally, students will be able to apply the methods studied to real data and interpret results correctly also from a theoretical point of view. c) Autonomy in assessment. Students develop analytical skills and capacity of facing different alternative approaches for solving actual empirical problems. d) Communication ability. Students learn technical language which is appropriate for the subject studied and that will be used at the oral and written exam, by means of practical exercises. e) Learning capacity. Students passing the exam are capable to extend the methodology studied also to other fields and derive conclusions.

Program (9 C.F.U., 72 hours, 6 C.F.U. 48 hours will cover same arguments but with lower degree of details)

Lessons spread on five main blocks. Every lesson will be briefly described from time to time on-line on the website “elearning2 Sapienza” also by uploading the accessory material used.

Part 1. Estimation methods: Panel Data Analysis

Part 2. Introduction of the analytical models (Production Economics, Productivity and Efficiency Measurements); Variables definition. Data and Measurement Issues. Initial evaluation of the datasets adopted for the applied analyses.

Part 3. Analytics for the parametric empirical approach: Cost, Profit (and in synthesis Revenue functions). Stochastic Frontier Models. Input Efficiency and Output Efficiency Indicators.

Part 4. Applied analysis to real data of the banking system, with R and STATA.

written and oral exam.

References (Available on-line (after enrollment in the course), website elearning2 Sapienza):

• Wooldridge, J.M, (2002), ”Econometric Analysis of Cross Section and Panel Data”, The MIT Press Cambridge, Massachusetts London, England (For panel Data).
• Maggi B. (2019), Lecture note (Some notions of Part I and Part II).
• Maggi B., Guida M., (2011), Modelling non-performing loans probability in the commercial banking system: efficiency and effectiveness related to credit risk in Italy, Empirical Economics, n. 2, pp 269-291 (For Dataset preparation).
• Pastor, J. M., Serrano L., (2005), "Efficiency, endogenous and exogenous credit risk in the banking systems of the Euro area.
• Berger A., Leusner J., Mingo J., (1997), “The efficiency of bank branches”, Journal of Monetary Economics, n° 1, n. 40, pp. 141-162.
• Berger A, Mester, L.J., (2001), “Explaining the dramatic changes in performance of US banks: technological change, deregulation, and dynamic changes in competition, WP n° 01-6, The Wharton Financial Institutions Center, University of Pennsylvania.
• Rolf F., Grosskopf, S., Lovell, C.A., Yaisawarng, S., (1993), “Derivation of Shadow Prices for Undesirable Outputs: A Distance Function Approach”, The Review of Economics and Statistics Vol. 75, No. 2, pp. 374-380
• Gallant, R., (1981), “On the bias of flexible functional forms and an essentially unbiased form”, Journal of Econometrics, n. 15, pp. 211-245.
• Measurement and Efficiency Issues in Commercial Banking, Chapter Author: Allen N. Berger, David B. Humphrey, Chapter URL: http://www.nber.org/chapters/c7237, Chapter pages in book: (p. 245 - 300)

Gallant, R., (1982), “Unbiased determination of production technologies”, Journal of Econometrics, n. 20, pp. 285-323.

• White, A., 1980. “Using Least squares to approximate unknown regression functions”.

Simper, R., 1999. Economies of scale in the Italian saving banking industry. Applied Financial Economics 9, 11—19.

• Battese Tim, D.S. Rao, G. Coelli, O’Donnell J., (2005), “An introduction to efficiency and productivity

analysis”, Springer.

References (Available on-line (after enrollment in the course), website elearning2 Sapienza):

• Battese Tim, D.S. Rao, G. Coelli, O’Donnell J., (2005), “An introduction to efficiency and productivity

analysis”, Springer.

• Wooldridge, J.M, (2002), ”Econometric Analysis of Cross Section and Panel Data”, The MIT Press Cambridge, Massachusetts London, England (For panel Data).
• Maggi B. (2019), Lecture note (Some notions of Part I and Part II).
• Maggi B., Guida M., (2011), Modelling non-performing loans probability in the commercial banking system: efficiency and effectiveness related to credit risk in Italy, Empirical Economics, n. 2, pp 269-291 (For Dataset preparation).
• Pastor, J. M., Serrano L., (2005), "Efficiency, endogenous and exogenous credit risk in the banking systems of the Euro area.
• Berger A., Leusner J., Mingo J., (1997), “The efficiency of bank branches”, Journal of Monetary Economics, n° 1, n. 40, pp. 141-162.
• Berger A, Mester, L.J., (2001), “Explaining the dramatic changes in performance of US banks: technological change, deregulation, and dynamic changes in competition, WP n° 01-6, The Wharton Financial Institutions Center, University of Pennsylvania.
• Rolf F., Grosskopf, S., Lovell, C.A., Yaisawarng, S., (1993), “Derivation of Shadow Prices for Undesirable Outputs: A Distance Function Approach”, The Review of Economics and Statistics Vol. 75, No. 2, pp. 374-380
• Gallant, R., (1981), “On the bias of flexible functional forms and an essentially unbiased form”, Journal of Econometrics, n. 15, pp. 211-245.
• Measurement and Efficiency Issues in Commercial Banking, Chapter Author: Allen N. Berger, David B. Humphrey, Chapter URL: http://www.nber.org/chapters/c7237, Chapter pages in book: (p. 245 - 300)

Gallant, R., (1982), “Unbiased determination of production technologies”, Journal of Econometrics, n. 20, pp. 285-323.

• White, A., 1980. “Using Least squares to approximate unknown regression functions”.

Simper, R., 1999. Economies of scale in the Italian saving banking industry. Applied Financial Economics 9, 11—19.

Learning goals The primary goal of the course on “Sample Surveys” is that student should learn the main problems and methods in sampling from finite populations. They should be able to formalize and plan the whole process of data collection and analysis in observational studies. In more detail, students should be able to plan a sample survey, to choose a sampling design, to plan the data collection, as well as to analyze real data and to estimate quantities of interest.
Knowledge and understanding After attending the course the students know and understand the main methodologies in planning a sample survey, as well as in dealing with non-sampling sources of error, such as nonresponses and missing values, measurement errors, list imperfections. Furthermore, students should be able to analyze real data and to estimate quantities of interests, such as means and proportions.
Applying knowledge and understanding At the end of the course the students are able to formalize and plan the whole process of data collection and analysis in observational studies. They should be able to manage the most important (i) sampling designs and (ii) point and interval estimators, as well as the main methodologies to deal with missing values, measurement errors, list imperfections. Moreover, they should be able to apply the methods to the data and to interpret the results.
Making judgements Students develop critical skills through the application of sampling and estimation methodologies to a wide range of contexts. They also develop the critical sense through the comparison of different solutions and the analysis of results.
Communication skills Students, through their study, should acquire the technical-scientific language of the discipline, to be used in their activity.
Learning skills Students who pass the exam have learned a method of analysis to be used in the data collection and analysis from finite populations.

• Aspetti fondamentali dei disegni di campionamento a probabilità variabile
• Probabilità di inclusione: proprietà e approssimazioni
• Alcuni esempi rilevanti: campionamento casuale semplice, campionamento casuale stratificato, campionamento a grappolo a uno stadio, campionamento a due stadi, campionamento sistematico, ppswr, ppswor, disegno di campionamento Midzuno-Lahiri
• Teoria dell'inferenza statistica nel campionamento casuale con approccio a popolazione fissa: aspetti di base
• Lo stimatore di Horvitz-Thompson e le sue proprietà. Il problema della stima della varianza: soluzioni esatte e approssimate.
• Disegni di campionamento con probabilità di inclusione prefissate: Poisson, Bernoulli, Pareto, Sampford, Poisson condizionato, disegni di campionamento.
• Disegni di campionamento bilanciati e algoritmo di cube sampling.
• Stimatori di calibrazione, con applicazioni alla post-stratificazione e alla stima nelle tabelle di contingenza. Algoritmo IPF.
• Stima della varianza mediante tecnica di linearizzazione.
• Ricampionamento basato su pseudo-popolazioni: aspetti generali e applicazioni alla stima della varianza.
• Errori non di campionamento: aspetti generali.
• Imperfezioni di frame. Campionamento "dual frame".
• Modelli di errori di misura. Effetti degli errori di misura.
• Mancate risposte risposte: aspetti generali. Metodologie per prevenire le mancate risposte. Metodologie per la ponderazione dei dati.
• Campionamento dei non rispondenti: l'approccio Hansen-Hurwitz in una prospettiva moderna.
• Tecniche di risposte randomizzate
• Stima delle probabilità di risposta attraverso gruppi di risposta omogenei
• Modelli di superpopolazione: aspetti di base
• Approcci basati sul disegno, sul modello e sull'assistenza al modello per l'inferenza dei parametri di superpopolazione
• Ignorabilità dei disegni di campionamento e conseguenze della non ignorabilità: l'emergere dell'inferenza assistita da modello.
• Verosimiglianza ponderata (Pseudo-log-verosimiglianza)
• Analisi di regressione per dati di indagine. Stimatore GREG
• Introduzione generale ai concetti di base (Popolazioni,

caratteri, parametri, Disegni campionari e loro proprietà. Esempi di disegni campionari). Stimatori e loro

proprietà. 8 ore

• Testi di riferimento: Lectures notes del docente
• Disegno semplice senza ripetizioni e relativi problemi inferenziali. 4 ore
• Testi di riferimento: Lectures notes del docente
• Stimatore di Horvitz-Thoompson. Stimatore di Hajek. 6 ore
• Testi di riferimento: Lectures notes del docente
• Disegno campionario stratificato. 2 ore
• Testi di riferimento: Lectures notes del docente
• Disegni campionari a grappolo e a 2 stadi. 4 ore
• Testi di riferimento: Lectures notes del docente
• Disegni campionari con differenti probabilità di inclusione (Sampford, Poisson condizionato, Pareto). Disegno bilanciato e sua implementazione mediante algoritmo cube sampling. 8 ore
• Testi di riferimento: Lectures notes del docente
• Stimatori per calibrazione. 6 ore
• Testi di riferimento: Lectures notes del docente
• Stima della varianza: linearizzazione e ricampionamento da pseudo-popolazioni. 6 ore
• Testi di riferimento: Lectures notes del docente
• Errori non campionari: aspetti generali. Errori di misura. 6 ore
• Testi di riferimento: Lectures notes del docente
• Studio del problema delle mancate risposte. 8 ore
• Testi di riferimento: Lectures notes del docente
• Modelli di superpopolazione, in particoilare di tipo regressione. Stimatore GREG e sue proprietà. 14 ore
• Testi di riferimento: Lectures notes del docente

Lo studente può scegliere tra due modalità di esame.

1. Redazione di un rapporto scritto su un argomento a scelta in un elenco fornito dal docente (il docente fornisce anche il materiale di letteratura su cui basare il rapporto scritto) + esame orale (discussione rapporto scritto + esame orale su argomenti del corso). 50% del voto finale è dato dal rapporto scritto e 50% dall'esame orale

2. Esame scritto + esame orale, entrambi su argomenti del corso. 50% del voto finale è dato dall'esame scritto e 50% dall'esame orale

Lectures notes + appunti del docente + esercizi con soluzioni

Materiali del corso disponibili all'indirizzo Google Classroom cjh63mmg

Learning goals
Statistical Machine Learning provides a rigorous introduction to the statistical and probabilistic principles that underpin modern artificial intelligence, including predictive and generative AI, and data-driven decision making. The course presents machine learning as a statistical discipline, where learning algorithms are viewed as methods for inference from data under uncertainty.
The course combines theoretical foundations with methodological and computational aspects. Students will develop an understanding of probabilistic modelling, statistical inference, optimization and generalization theory, together with the practical skills required to implement, evaluate and compare modern learning algorithms. Representative methodologies include kernel methods, deep neural networks, probabilistic generative models such as denoising diffusion probabilistic models (DDPMs), and modern uncertainty quantification techniques including conformal prediction. Throughout the course, particular emphasis is placed on principled model assessment, uncertainty quantification, and the interpretation of predictive models.
Theoretical results are complemented by programming assignments and a substantial project based on real datasets, allowing students to connect statistical principles with computational implementation using modern open-source scientific computing environments. Knowledge and understanding Upon successful completion of the course, students will understand the statistical principles underlying modern machine learning and their role in predictive and generative artificial intelligence. They will be able to interpret learning algorithms within a unified probabilistic framework, understanding how statistical modelling, inference, optimization and regularization jointly determine predictive performance and generalization. Students will understand the assumptions, mathematical foundations and computational characteristics of the principal supervised and unsupervised learning methodologies, including kernel methods, (deep) neural networks and probabilistic generative models. They will also understand the theoretical principles governing model selection, validation, complexity control/regularization, and uncertainty quantification, together with the statistical tools used to assess and compare learning algorithms. Finally, students will acquire familiarity with modern scientific computing environments for implementing, analysing and experimentally evaluating statistical machine learning methods. Applying knowledge and understanding Upon successful completion of the course, students will be able to formulate complex data analysis problems within an appropriate statistical learning framework and identify suitable modelling strategies based on the characteristics of the data and the objectives of the analysis. They will acquire the practical skills required to implement, train and compare modern machine learning methods, integrating statistical modelling with computational techniques to build complete data analysis pipelines. Students will learn to assess predictive performance using principled statistical methodologies, quantify predictive uncertainty, and interpret the results in light of the assumptions and limitations of the adopted models. Programming assignments and a final project will provide hands-on experience with real-world datasets, enabling students to bridge theoretical concepts with practical implementation and to develop reproducible machine learning solutions. Making judgements Upon successful completion of the course, students will have developed the ability to critically evaluate statistical learning methodologies from both theoretical and empirical perspectives. They will be able to assess whether the assumptions underlying a learning model are appropriate for a given application, compare competing modelling approaches, and interpret empirical results in light of statistical evidence. Particular emphasis is placed on understanding the role of uncertainty in statistical learning, enabling students to distinguish between predictive performance, statistical significance, and confidence in model outputs. Students will develop the ability to critically assess the reliability, robustness and limitations of learning algorithms when applied to complex real-world data. Communication skills Students will develop the ability to communicate statistical and machine learning methodologies effectively through written reports and oral presentations. They will learn to present methodological choices, experimental results, and quantitative evidence in a clear, rigorous and reproducible manner, adapting the level of technical detail to both specialist and non-specialist audiences. The final project will also foster the ability to discuss modelling decisions, interpret experimental findings, and provide constructive scientific feedback on the work of peers. Learning Skill The course aims to develop the independent learning skills required for continued study and research in statistical machine learning and artificial intelligence. By combining theoretical analysis with computational practice, students will acquire the ability to understand new methodologies from the scientific literature, critically evaluate their statistical foundations, and adapt them to novel application domains. Particular emphasis is placed on developing a probabilistic mindset for reasoning under uncertainty, enabling students to understand emerging machine learning methodologies, integrate advances from the rapidly evolving AI landscape, and pursue lifelong learning with methodological rigour and intellectual independence.

Obiettivo formativo dell’insegnamento è l'apprendimento da parte degli studenti dei fondamenti del calcolo delle probabilità e dell’inferenza statistica. Conoscenza e capacità di comprensione Alla fine del corso gli studenti conoscono e comprendono come formalizzare l’incertezza e come fare inferenza su parametri non noti. Capacità di applicare conoscenza e comprensione Gli studenti apprendono come impostare un problema di probabilità o inferenza. Autonomia di giudizio La discussione dei vari metodi, anche con lavori di gruppo, fornisce agli studenti le capacità necessarie per analizzare criticamente, ed in autonomia, situazioni reali. Abilità comunicativa Gli studenti acquisiscono gli elementi di base per ragionare, e far ragionare, in termini quantitativi su problemi di incertezza ed inferenza. Capacità di apprendimento Gli studenti che superano l’esame sono in grado di applicare i metodi appresi in diversi contesti applicativi.

Learning goals The course provides a broad introduction to stochastic processes. In particular the aim is - to give a rigorous introduction to the theory of stochastic processes, - to discuss the most important stochastic processes in some depth with examples and applications, - to give the flavour of more advanced work and applications, - to apply these ideas to answer basic questions in several applied situations including biology, finance and search engine algorithms.
Knowledge and understanding At the end of the course the students will be familiar with the basic concepts of the theory of stochastic processes in discrete and continuous time and will be able to apply various techniques to study stochastic models that appear in applications.
Applying knowledge and understanding At the end of the course the students will have the tools to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space. The students will have the tools to solve simple applied problems in new environments and broader contexts.
Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. The student will also acquire the necessary language skills to start reading academic books on the topic and research papers.
Communication skills The students will acquire the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will also acquire the rationale behind the stochastic model studied (e.g. the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour...) that is necessary to communicate to specialist and non-specialist audiences.
Learning skills The students will acquire the methodology and the language to study in a manner that may be largely autonomous and to apply the methodology to the subsequent studies in the area of statistics and finance.

Random walks (about 18 hours)

Definition, Markovianity, temporal and spatial invariance, random walks with reflecting and absorbing barriers, reflection principle, ballot theorem, distribution of the maximum, hitting time theorem, first and second arc sine law, random walks and generating functions, short introduction on Black–Scholes model.

Brownian motion (about 18 hours)

Definition and existence, Brownian motion as a limit of a simple random walk, path properties of Brownian motion, Brownian motion as a strong Markov process, transience and recurrence.

Branching processes (about 6 hours)

Definition, expectation and variance of the population size, geometric branching, probability of extinction of the population.

Markov chains (about 18 hours)

Definition, homogeneous Markov chains, transition matrix, examples of Markov chains, classification of states, classification of chains, stationary distribution and limit theorem, chains with finitely many states, short introduction on Monte Carlo method,

MCMC and search engine algorithms.

Poisson processes (about 6 hours)

Definition and main properties.

The examination consists of three questions on three different topics studied during the course. The students are required to answer the questions during the written exam showing that they have acquired the intuition and the technical language, and that they are able to present the topics and the proofs with all necessary details. The oral exam consists in a discussion of the written exam. 80% of the final grade is based on the written exam.

Recommended books:

• G.R. Grimmett and D.R. Stirzaker. Probability and Random Processes. 3rd edn, OUP, 2001
• P. Mörters and Y. Peres. Brownian Motion. Cambridge Series in Statistical and Probabilistic Mathematics, 2010

Helpful books:

• D. Williams. Probability with Martingales. CUP, 1991

Teaching material is also delivered through e-learning platform "moodle"

Recommended books:

• G.R. Grimmett and D.R. Stirzaker. Probability and Random Processes. 3rd edn, OUP, 2001
• P. Mörters and Y. Peres. Brownian Motion. Cambridge Series in Statistical and Probabilistic Mathematics, 2010

Helpful books:

• D. Williams. Probability with Martingales. CUP, 1991

Teaching material is also delivered through e-learning platform "moodle"

L'obiettivo formativo primario dell’insegnamento è l'apprendimento da parte degli studenti dei principali problemi e metodi legati alle indagini statistiche. Gli studenti devono apprendere i principali elementi teorici che conducono alla organizzazione delle indagini e ai problemi legati all’errore di non osservazione (campionamento) e all’errore di osservazione (strumento di rilevazione). Una particolare attenzione verrà dedicata ai temi legati al dato soggettivo e alla consolidata teoria che conduce ad una corretta rilevazione e interpretazione degli stessi.
Obiettivi specifici a) Conoscenze Al termine del corso, gli studenti conoscono e comprendono i principi delle indagini statistiche (soprattutto nell’ambito della Statistica Ufficiale) e delle fasi operative che lo compongono.
b) Competenze Al termine del corso gli studenti sono in grado di definire un progetto di rilevazione definendo le diverse fasi che lo compongono.
c) Autonomia di giudizio Le conoscenze e le competenze sviluppate consentono agli studenti di osservare in maniera critica progetti di indagini esistenti e di valutare la possibilità di modifiche costruttive e finalizzate al loro miglioramento.
d) Abilità comunicativa Gli studenti, attraverso lo studio e lo svolgimento di esercizi pratici, acquisiscono il linguaggio tecnico-scientifico della disciplina, con particolare riferimento alla rilevazione di dati individuali e soggettivi. Le abilità comunicative vengono sviluppate anche attraverso attività di gruppo.
e) Capacità di apprendimento Gli studenti che superano l’esame hanno appreso la metodologia delle rilevazioni statistiche. Ciò consente loro di di affrontare lo studio dei successivi insegnamenti con una consapevolezza legata soprattutto alle rilevazioni nell’ambito della Statistica Ufficiale.

L'indagine moderna: metodologia e analisi

(A) Indagine statistica

A. Il disegno del sondaggio

i. Disegno longitudinale

ii. Disegno in sezione trasversale

B. Complessità delle indagini nei contesti sociali

C. Fasi di rilievo e progettazione

(B) approcci di campionamento applicati

A. Rappresentatività nel contesto sociale

B. Approcci di campionamento probabilistici e non probabilistici

C. Definire la dimensione dei campioni

(C) Questionario

A. Progettazione del questionario

B. Questioni pratiche nella definizione di domande e risposte

C. Raccolta dati: diversi approcci alla raccolta dati (tra cui web e modalità mista)

(D) Problemi cruciali dell'indagine

A. Errori di non osservazione

i. Errori di campionamento

ii. Errori di mancata risposta: osservazioni mancanti (come prevenirli, gestirli e risolverli)

B. Errori di osservazione

i. Errore di misura: teoria e implicazioni

C. metodologia di indagine e statistiche ufficiali

D. problemi comparativi

(E) Dati dell'indagine: preparazione dei dati per l'analisi statistica

A. Verifica dei dati (accuratezza, dati mancanti, valori anomali)

B. Gestione dei dati (trasformazione, modifica, ricodifica, ecc.)

C. Sintetizzare i dati

(F) Approfondimento: i modelli di scaling

L'esame di profitto si baserà sulla stesura di un progetto in aggiunta alla prova orale

Since the course will be delivered in English, all information will be provided here in that language.

Since the course will be delivered in English, all information will be provided here in that language.

Learning goals The main objective of this course is to introduce students to the statistical work of International Organizations and their contribution to the production of International Official Statistics. The differences between the statistical work of International Organizations and those of National Statistical Organizations, as well as the specific contribution of International Organizations to the global statistical system will be explained in detail.
Knowledge and understanding The course is organized in two distinct parts: the first part of the course (12 lessons) will provide students with an introduction to the Global Statistical System, its key institutions and coordination mechanism, and the statistical work of International Organizations; the second part of the course (12 lessons) will familiarize students with the main statistical techniques used in international organizations for the production of official statistics, through hands-on practical training. In particular, the first part of the course will describe: 1) How the international statistical system is organized: what are the main actors/Institutions involved, what are the key governance bodies, what are the key international statistical frameworks (SNA, BoP, IMTS, SEEA) and standards (including standards on international classifications; code-lists, flag systems) that guide the statistical work at national, regional and global levels; how international statistical standards are developed and implemented. Special focus on the System of National Accounts. 2) The statistical work of International Statistical Organizations: what are the key data sources and how the data flows from national to international organizations is structured; the main quality frameworks used at international level and how the quality of input data is assessed; the techniques used for data validation, editing and imputation; data discrepancies between national and international databases: how they can be explained and addressed; the data dissemination practices of international organizations, including the dissemination of microdata and the challenges it poses in terms of protection of data confidentiality. 3) The demand for new statistics resulting from the 2030 Agenda for Sustainable Development. The evolution of the concept of sustainable development and the ambition of the 2030 Agenda; The Global SDG Indicator Framework and its governance mechanisms; the role of the international Organizations as Custodian Agencies, the data flows between countries and International Organizations Applying knowledge and understanding At the end of the course, students will have the necessary instruments be start working in an international organization, having acquired an in-depth knowledge of the institutional, methodological, and technical aspect of its statistical work. Moreover, students will become familiar with the main statistics published in international databases; the compilation methodology and time series of selected SDG indicators; the main techniques used for the compilation of index numbers, latent variables, composite indices, trend indices and seasonally adjusted data.
Making judgements At the end of the course, students will be able to apply their skills in analyzing and interpreting official statistics published by international organizations.
Communication skills At the end of the course, students will acquire the ability of discussing statistical problems in an international environment and presenting oral and written reports of their practical analyses.
Learning skills At the end of the course, students will be able to further improve their skills and knowledge of international official statistics by self-study and consultation of international organization databases, which will be helpful for future academic and professional activities.

Learning goals Knowing how to reorganize multidimensional data with a complex structure for their statistical analysis. Starting from the multivariate statistical methodologies, knowing how to realize complex strategies of analysis that have an easy interpretation. To be able to make decisions based on empirical evidence giving appropriate answers to corporate information requests. Knowing how to extract relevant information from large data (big data).
Knowledge and understanding Knowledge of advanced multivariate statistical methodologies and advanced formal data analysis strategies with a model-based statistical approach.
Applying knowledge and understanding Understanding the most appropriate complex techniques to be able to make decisions based on empirical evidence, respond to corporate information requests and be able to extract relevant information from the observed data.
Making judgements Students develop critical skills through the application of complex multivariate statistical methodologies. They learn to critically interpret the results obtained by applying the procedures to real data sets.
Communication skills Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which must be used in the world of work. Communication skills are also developed through group activities.
Learning skills Students who pass the exam have learned a method of analysis that allows them to face, the world of work having acquired advanced statistical analysis tools for complex and large data.

General goals Knowledge at an intermediate and advanced level of the main issues in Bayesian statistics. Ability to apply Bayesian statistical techniques to applicative context.
Knowledge and understanding Knowledge and understanding of the Bayesian approach to statistical inference, of its models and of its methodologies
Applying knowledge and understanding Ability to apply Bayesian statistical methods for inferential problems in real-data problems
Making judgements Ability of choosing appropriate Bayesian methods and models in different inferential problems
Communication skills Ability of communicating results of the analyses in written and oral form
Learning skills Students acquire skills useful to approach more advanced topics in Bayesian inference, Advanced data analysis, Statistical computing and Mathematical statistics

General goals Knowledge at an intermediate and advanced level of the main issues in Bayesian statistics. Ability to apply Bayesian statistical techniques to applicative context.
Knowledge and understanding Knowledge and understanding of the Bayesian approach to statistical inference, of its models and of its methodologies
Applying knowledge and understanding Ability to apply Bayesian statistical methods for inferential problems in real-data problems
Making judgements Ability of choosing appropriate Bayesian methods and models in different inferential problems
Communication skills Ability of communicating results of the analyses in written and oral form
Learning skills Students acquire skills useful to approach more advanced topics in Bayesian inference, Advanced data analysis, Statistical computing and Mathematical statistics

Learning goals. The different techniques existing for Big Data management will be illustrated, with a particular emphasis on NoSQL databases. The course will also deal with the problem of collecting Big Data from various sources such as from the web or from the online social networks. This will require also the introduction of the different formats that are commonly used to encode unstructured, semi-structured and structured data and of the different techniques that can be used to automate their processing. Successively, pre-processing techniques, including denoising and imputation of missing data, will be considered. Then, the course will treat dimensionality reduction techniques, based on feature extraction and feature selection. Finally, some statistical learning models, supervised and unsupervised, for the analysis of Big Data, will be presented. Real-world problems will be addressed during the course using suitable software.
Knowledge and understanding. The student will learn as to apply some statistical learning techniques for dimensionality reduction, based on feature extraction and feature selection. Moreover, he will know and understand some powerful statistical learning models, supervised and unsupervised, to analyse Big Data.
Applying knowledge and understanding. The student will be able to manage Big Data collected from various sources. He will learn as to apply dimensionality reduction techniques, based on feature extraction and feature selection. Moreover, he will be able to choose and apply some powerful statistical learning models to analyse Big Data.
Making judgements. Students will develop critical skills through the application of a wide range of machine learning and statistical models. They also will develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They will learn to critically interpret the results obtained by applying the procedures to real data sets.
Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the intermediate and final written tests and in the oral tests. Communication skills are also developed through group activities.
Learning skills. Students who pass the exam will have learned an analytical approach that allows them to tackle Big Data analysis with statistical models and machine learning methods.

General learning goals - Define what subset of Big Data can be used in Official Statistics and what domains of Official Statistics can be enriched through the availability of new data sources - Establish how new data sources can be used in Official Statistics, by taking into account challenges, needs and risks in this exercise - Definition of the role of Big Data in the context of Official Statistics - Establish how to frame the measurement of social, demographic and economic phenomena through Big Data by considering challenges, needs and risks
Knowledge and understanding Knowledge and understanding of statistical methods to handle Big Data in official statistics
Applying knowledge and understanding Ability to apply statistical methods for official statistics problems with emphasis on Big Data
Making judgements Ability of choosing appropriate methods in different problems in official statistics with emphasis on Big Data
Communication skills Ability of communicating results of the analyses in official statistics with emphasis on Big Data
Learning skills Students acquire skills useful to approach more advanced topics in official statistics and Big Data management

Learning goals The main goal of the course is to learn about common general computational tools and methodologies to perform reliable statistical analyses. Students will be able - to understand the theoretical foundations of the most important methods; - to appropriately implement and apply computational statistical procedures; - to interpret the results deriving from their applications to real data.
Knowledge and understanding After attending the course, students will know and understand the most important computational techniques in statistical analysis. In addition, students will be able to appropriately implement the learned tools with the statistical software R and to develop original ideas often in a research context.
Applying knowledge and understanding At the end of the course, students will be able to formalize statistical problems from a computational point of view, to apply the learned methods to solve them, also in contexts not covered in the lessons, and to interpret the results deriving from their applications to real data.
Making judgements Students will develop critical skills through the application of computational methodologies to a wide range of statistical problems and through the comparison of alternative solutions to the same problem by using different tools. Furthermore, they will learn to interpret critically the results obtained by applying procedures to real datasets.
Communication skills By studying and carrying out practical exercises, students will acquire the technical-scientific language of the discipline, which must be suitably used in the final written test. Communication skills will be also developed through group activities.
Learning skills Students who pass the exam have learned computational techniques useful in the statistical analysis and to work self-sufficiently to face with the complexity of the statistical problems.

Introduction to computational statistics and R programming (2 hours)

Methods for Generating Random Variables (6 hours)

Visualization of Multivariate Data (2 hours)

Monte Carlo Integration and Variance Reduction (8 hours)

Monte Carlo methods in Inference (6 hours)

Bootstrap and Jackknife (12 hours)

Permutation Tests (6 hours)

Optimization and solving nonlinear equations (6 hours)

EM optimization methods (2 hours)

Fuzzy clustering algorithms (10 hours) [only for 9 ECTS course]

Group projects (12 hours) [only for 9 ECTS course]

6 CFU EXAM

The learning assessment consists of a written exam, carried out in a computer laboratory (duration: 2 hours), which includes:

• Theoretical questions – 2/3 of the final grade
• Practical questions in R – 1/3 of the final grade

To pass the written exam, students must pass both components (theoretical and R-based questions).

9 CFU EXAM

The learning assessment comprises:

1. Written exam (6 CFU), carried out in a computer laboratory (duration: 2 hours), consisting of:

• Theoretical questions – 2/3 of the written exam grade
• Practical questions in R – 1/3 of the written exam grade

To pass the written exam, students must obtain a passing grade in both components (theoretical questions and R-based practical questions).

2. Group project (3 CFU)

• M. L. Rizzo (2008): Statistical Computing with R. Chapman & Hall, Boca Raton.
• G. H. Givens, J. A. Hoeting (2005): Computational Statistics. Wiley & Sons, Hoboken.
• B. Efron, B.J. Tibshirani (1993): An Introduction to the Bootstrap. Chapman and Hall/CRC.

General Managers worldwide, beyond their personal experience, rely more and more on the use of quantitative decision models which allow to take advantage of today’s data availability. Morover, new computational tools, including algorithms, cloud computing and distributed processing, make it possible to both develop and compute analytical models in a very short time, meeting the requirement of practical applications and often using real time data. Data Driven Decision Making is the new paradigm for managers to make better, evidence based, more rational, transparent and reliable decisions. In this context, the primary educational objective of the course is students' learning of the main decision problems that arise in real world and the quantitative methods to model them and to feed them with adequate data. Students must also be able to correctly use, for decision-making and management purposes, computer tools to analyze data generated by real problems in different contexts (e.g. service management, marketing, transportation, operations management and production, and finance) through the analysis of several case studies.
Specific objectives
a) Knowledge and ability to understand After attending the course the students know and classify the main decision problems arising in real world organization and the main analytical methods (decision and optimization models and algorithms) to be used to support a Manager during his/her decision process.
b) Ability to apply knowledge and understanding At the end of the course the students are able to formalize real problems in terms of decision problems and to apply the specific methods taught in the course to solve them. They are also able to classify the type of problem to it the most appropriate quantitative method, experimenting the effectiveness for decisional purposes also on real problems.
c) Autonomy of judgment Students develop critical skills through the application of modeling, decision analysis and multi objective optimization methodologies to a broad set of practical problems. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using methods of analysis and realistic scenarios different from each other. They learn to critically interpret the results obtained by applying the procedures to real data sets.
d) Communication skills Students, through the study and the carrying out of practical exercises, acquire the technical- scientific language of the course, which must be properly used both in the intermediate and final written tests and in the oral tests. Communication skills are also developed through group activities.
e) Learning ability Students who pass the exam have learned methods of decision analysis and multiobjective optimization that allow them to face, decision-making problems and optimization on complex organizations.

Programma

Part I Introduction

Role of Data and Models in Decision Making Process

Platform and Network architectures for DDDM

Analytics Tools for DM

Choice and Optimization Models

Part II Decision Models

Combinatorial Representation of Preferences

Ordinal Value Functions

Multi-Attribute Value Theory and Machine Learning Models

Multicriteria Decision Making Methods

Part III Multi-Objective Optimization Models

Linear Programming with Multiple Criteria

Goal Programming

Multi-Objective Combinatorial Optimization

Data Sensitivity Analysis in the Objective Space

Part IV Multiple Decisor Makers and Agent Based Decision Models

Combinatorial Models for Collective Choice

Aggregation of Preferences

Metric Approach to Collective Choice

Game Theory Models

Part V Case Studies

Finance, Sports and Games, Transportation, Healthcare, Management Operations, Crime, Internet, Netflix, ecc.

L'esame consiste in un test scritto con alcuni esercizi ispirati a quelli svolti durante il corso e alcune domande a risposta aperta sugli argomenti trattati a lezione.

1. D. Bertsimas, and R. Freund. Data, Models, and Decisions: The Fundamentals of Management Science. Dynamic Ideas, Wiley, 2004. ISBN: 9780975914601.

2. M. Ehrgott, Multicriteria Optimization, Springer, 2005.

3. A. Ishizaka, P. Nemery, Multi-criteria Decision Analysis: Methods and Software, ISBN: 978-1-119-97407-9, WIley, 2013.

4. Software manuals available on line and on the e-learning platform

General Aim The primary educational goal of the course is students' learning of the main concepts and basic methods of Demography.
Knowledge and understanding After attending the course, the students know and understand the main international demographic sources and the measures to describe the population processes.
Applying knowledge and understanding At the end of the course, the students are able to apply the learned methods to the real data, and to understand the results of these applications.
Making judgements Students develop critical skills through the application of different indicators and measures to a wide range of case studies from different countries, and learn to critically interpret the results.
Communication skills Students, through the study and the carrying out of practical exercises, acquire the technical-scientific language of the discipline, which must be opportunely used in the final oral examination. Communication skills are also developed through group activities.
Learning skills Students who pass the exam have learned the skills necessary to address the study of more complex methods and models in subsequent teachings of demographic area.

Il corso si concentra sull'analisi dei trend demografici emergenti in una prospettiva comparativa e internazionale. Strumenti analitici per lo studio della dinamica della popolazione. Metodi di base e indicatori demografici. Dinamiche a lungo termine della popolazione mondiale. La transizione demografica. Popolazione contemporanea nei paesi sviluppati: invecchiamento della popolazione e bassa fertilità. Casi di studio della popolazione contemporanea nei paesi in via di sviluppo. Migrazioni internazionali. Previsioni e proiezioni di popolazione.

Prova orale al termine dell'insegnamento volta ad accertare sia l'acquisizione degli aspetti teorici che l’abilità di applicare gli strumenti demografici di base (50%). Valutazione di un miniproject su tema a scelta degli studenti (50%).

• PRESTON, HEUVELINE, GUILLOT, Demography. Modelling Population Processes (2001), Blackwell
• M. LIVI BACCI, A Concise History of World Population, Wiley & Sons Ltd., 2017.
• Materiale didattico reso disponibile tramite piattaforma Classroom.

CASELLI G., VALLIN J. & WUNSCH G. (2006), Demography – Analysis and Synthesis. A Treatise in Population. Elsevier.

De SANTIS G. (2010) Demografia, Il Mulino, Bologna.

M. LIVI BACCI (1999), Introduzione alla demografia, Loescher Ed., Torino.

M. Livi Bacci (2024) Storia minima della popolazione del mondo. E uno sguardo al futuro. Il Mulino, Bologna.

M. LIVI BACCI (2017), A Concise History of World Population, Wiley & Sons Ltd.

M. LIVI BACCI (2017) Our Shrinking Planet, Wiley and Sons Ltd.

J.H. LUNDQUIST, D.L. ANDERTON, D. YAUKEY (2015), Demography. The Study of Human Population, Waveland Press.

PRESTON, HEUVELINE, GUILLOT, Demography. Modelling Population Processes (2001), Blackwell.

The primary educational objective of the laboratory is students' learning and practice of the main tools for Data Driven Decision Making, that is the use of computer tools to analyze data and formalize optimization or decision models and produce decisions that create value.
Knowledge and ability to understand After attending the laboratory, students will be able to use decision support methods (like, the Analytical Hierchical Process), optimization solvers (like CPLEX or Gurobi) and computer algorithms for modelling multicriteria decision and optimization problems.
Ability to apply knowledge and understanding The models are formalized in the realm of problems. The most appropriate quantitative method, experimenting with the effectiveness of the problem.
Autonomy of judgment Students develop critical skills through the application of modeling, analysis and optimization to a broad set of decision problems. They also develop the critical sense through the comparison between alternative solutions to the same problem using methods of analysis and realistic scenarios different from each other. They learn to critically interpret the results obtained by applying the procedures to real data sets.
Communication skills Students, through the study and the carrying out of the practical exercises, acquire the technical-scientific language of the course, which should be used in the tests. Communication skills are also developed through group activities.
Learning ability Students who pass the exam have acquired the main methods of analysis and optimization of decision problems that allow them to face decision-making and quantitative management in competitive nowadays enterprises.

General

Managers worldwide, beyond their personal experience, rely more and more on the use of quantitative decision models which allow to take advantage of today’s data availability. A variety of different computational model and software platform is available to transform data in valuable evaluation and decisions

Our aim is to give to the student the experience of processing data to feed evaluation and optimization software tools applied to a number of contexts (e.g. service management, marketing, transportation, operations management and production, and finance) through the analysis of several case studies.

List_of_topics

Part I Introduction

Quantitative Models for Collective Decision Making: The Balanced Scorecard

Analytics Tools and software for Decision Making

Part II Decision Models

Multi Attribute and Multicriteria Methods and Applications:

Practical use of the Analytical Hierarchy Process (AHP)

Ranking methods and software

Part III MultiObjective Optimization Models

Linear Programming with Multiple Criteria - Goal Programming with CPLEX

Multi-Objective Combinatorial Optimization with CPLEX

Data Sensitivity Analysis in the Objective Space with CPLEX

Part IV Multiple Decisors and Agent_Based Decision Models

Aggregation of Preferences: real and practical cases

Metric Approach to Collective Choice: real and practical cases

Recommendation Systems: real and practical cases

The laboratory grade will be based on a project developed in laboratory and completed as homework assignment.

1. D. Bertsimas, and R. Freund. Data, Models, and Decisions: The Fundamentals of Management Science. Dynamic Ideas, Wiley, 2004. ISBN: 9780975914601.

2. M. Ehrgott, Multicriteria Optimization, Springer, 2005.

3. A. Ishizaka, P. Nemery, Multi-criteria Decision Analysis: Methods and Software, ISBN: 978-1-119-97407-9, WIley, 2013.

4. Henggeler Antunes, Carlos, Alves, Maria João, Clímaco, João Multiobjective Linear and Integer Programming

5. Dimitris Bertsimas, Allison O'Hair: The Analytics Edge, 2017

Learning goals The main goal is acquiring advanced modelling techniques for mutivariate economic data. Students are expected to understand the theoretical foundations of the methods studied and to apply them to real datasets.
Knowledge and understanding The focus of the course will on the Vector Autoregressive (VAR) model in stationary and non stationary settings, using both asymptotic and simulation (bootstrap) inference
Applying knowledge and understanding After the course students will be able to specify a VAR model, evaluate if is adequate to the dataset of interest , use for estimating causal relationship and formatulate forecasts
Making judgements Learning how to judging the adequacy of the models and assessing the uncertainty of the estimated relationships and forecasts will be an essential part of the course
Communication skills Learning to communicate the results of the estimation process both in oral and written form will be an essential part of the course
Learning skills The models object of the course are essential parts of the most advanced and complex models used in quantitative economic analysis, which the students will then be able to tackle.

Il libro di testo di riferimento è

Guidolin, M., Pedio, M., Essentials of Time Series for Financial Applications, 1st Edition, Academic Press, May 2018.

In particolare tratteremo:

i) Vector Autoregressive Moving Average (VARMA) Models, (Ch. 3)

ii) Cointegration, (Ch. 4)

iii) Multivariate GARCH and Conditional Correlation Models, (Ch. 6)

esercizi valutati durante il corso e prova scritta e orale finale

Guidolin, M., Pedio, M., Essentials of Time Series for Financial Applications, 1st Edition, Academic Press, May 2018.

Tsay (2010) Analysis of Financial Time Series, Wiley

Hamilton (1994) Time Series Analysis, Princeton University Press

General Targets: Prior educational teaching concern is the students’ understanding of the main (Economic Statistics Modeling) problems and methods for Panel Data making use of parametric estimation. Here the empirical focus is on individuals represented by Decisional Making Units (DMU). More specifically, these are banks typically involved in the European (and also international) banking system. The course will focus on managerial problems of these firms by studying equations such as cost (mostly) and profit functions which are relevant to asses on the Efficiency of banks. Furthermore, students should know both how to solve analytical problems, in order to apply the appropriate methodology, and to interpret results obtained from empirical applications to actual data. Specific Targets: a) Knowledge and capability in understanding. After attending the course, students know and understand main problems of Panel Data. In particular, the course will account for the logic for building empirical models, related to the underlying economic theory (and the consequent subdivisions in endogenous and exogenous variables), with one or more equations in order to evaluate the degree of efficiency of a typical Decisional Making Unit (here the bank and possibly the insurance company). We will study the main estimation methods of Panel Data for solving efficiency problems pertaining a firm traditionally operating in the private sector. b) Capability of applying knowledge and comprehension At the end of the course students are able to formalize and solve problems by means of specific methods as well as treating fundamental models of Panel Data to answer questions on the Efficiency and Productivity Analysis for the banking system. Finally, students will be able to apply the methods studied to real data and interpret results correctly also from a theoretical point of view. c) Autonomy in assessment. Students develop analytical skills and capacity of facing different alternative approaches for solving actual empirical problems. d) Communication ability. Students learn technical language which is appropriate for the subject studied and that will be used at the oral and written exam, by means of practical exercises. e) Learning capacity. Students passing the exam are capable to extend the methodology studied also to other fields and derive conclusions.

Program (9 C.F.U., 72 hours, 6 C.F.U. 48 hours will cover same arguments but with lower degree of details)

Lessons spread on five main blocks. Every lesson will be briefly described from time to time on-line on the website “elearning2 Sapienza” also by uploading the accessory material used.

Part 1. Estimation methods: Panel Data Analysis

Part 2. Introduction of the analytical models (Production Economics, Productivity and Efficiency Measurements); Variables definition. Data and Measurement Issues. Initial evaluation of the datasets adopted for the applied analyses.

Part 3. Analytics for the parametric empirical approach: Cost, Profit (and in synthesis Revenue functions). Stochastic Frontier Models. Input Efficiency and Output Efficiency Indicators.

Part 4. Applied analysis to real data of the banking system, with R and STATA.

written and oral exam.

References (Available on-line (after enrollment in the course), website elearning2 Sapienza):

• Wooldridge, J.M, (2002), ”Econometric Analysis of Cross Section and Panel Data”, The MIT Press Cambridge, Massachusetts London, England (For panel Data).
• Maggi B. (2019), Lecture note (Some notions of Part I and Part II).
• Maggi B., Guida M., (2011), Modelling non-performing loans probability in the commercial banking system: efficiency and effectiveness related to credit risk in Italy, Empirical Economics, n. 2, pp 269-291 (For Dataset preparation).
• Pastor, J. M., Serrano L., (2005), "Efficiency, endogenous and exogenous credit risk in the banking systems of the Euro area.
• Berger A., Leusner J., Mingo J., (1997), “The efficiency of bank branches”, Journal of Monetary Economics, n° 1, n. 40, pp. 141-162.
• Berger A, Mester, L.J., (2001), “Explaining the dramatic changes in performance of US banks: technological change, deregulation, and dynamic changes in competition, WP n° 01-6, The Wharton Financial Institutions Center, University of Pennsylvania.
• Rolf F., Grosskopf, S., Lovell, C.A., Yaisawarng, S., (1993), “Derivation of Shadow Prices for Undesirable Outputs: A Distance Function Approach”, The Review of Economics and Statistics Vol. 75, No. 2, pp. 374-380
• Gallant, R., (1981), “On the bias of flexible functional forms and an essentially unbiased form”, Journal of Econometrics, n. 15, pp. 211-245.
• Measurement and Efficiency Issues in Commercial Banking, Chapter Author: Allen N. Berger, David B. Humphrey, Chapter URL: http://www.nber.org/chapters/c7237, Chapter pages in book: (p. 245 - 300)

Gallant, R., (1982), “Unbiased determination of production technologies”, Journal of Econometrics, n. 20, pp. 285-323.

• White, A., 1980. “Using Least squares to approximate unknown regression functions”.

Simper, R., 1999. Economies of scale in the Italian saving banking industry. Applied Financial Economics 9, 11—19.

• Battese Tim, D.S. Rao, G. Coelli, O’Donnell J., (2005), “An introduction to efficiency and productivity

analysis”, Springer.

References (Available on-line (after enrollment in the course), website elearning2 Sapienza):

• Battese Tim, D.S. Rao, G. Coelli, O’Donnell J., (2005), “An introduction to efficiency and productivity

analysis”, Springer.

• Wooldridge, J.M, (2002), ”Econometric Analysis of Cross Section and Panel Data”, The MIT Press Cambridge, Massachusetts London, England (For panel Data).
• Maggi B. (2019), Lecture note (Some notions of Part I and Part II).
• Maggi B., Guida M., (2011), Modelling non-performing loans probability in the commercial banking system: efficiency and effectiveness related to credit risk in Italy, Empirical Economics, n. 2, pp 269-291 (For Dataset preparation).
• Pastor, J. M., Serrano L., (2005), "Efficiency, endogenous and exogenous credit risk in the banking systems of the Euro area.
• Berger A., Leusner J., Mingo J., (1997), “The efficiency of bank branches”, Journal of Monetary Economics, n° 1, n. 40, pp. 141-162.
• Berger A, Mester, L.J., (2001), “Explaining the dramatic changes in performance of US banks: technological change, deregulation, and dynamic changes in competition, WP n° 01-6, The Wharton Financial Institutions Center, University of Pennsylvania.
• Rolf F., Grosskopf, S., Lovell, C.A., Yaisawarng, S., (1993), “Derivation of Shadow Prices for Undesirable Outputs: A Distance Function Approach”, The Review of Economics and Statistics Vol. 75, No. 2, pp. 374-380
• Gallant, R., (1981), “On the bias of flexible functional forms and an essentially unbiased form”, Journal of Econometrics, n. 15, pp. 211-245.
• Measurement and Efficiency Issues in Commercial Banking, Chapter Author: Allen N. Berger, David B. Humphrey, Chapter URL: http://www.nber.org/chapters/c7237, Chapter pages in book: (p. 245 - 300)

Gallant, R., (1982), “Unbiased determination of production technologies”, Journal of Econometrics, n. 20, pp. 285-323.

• White, A., 1980. “Using Least squares to approximate unknown regression functions”.

Simper, R., 1999. Economies of scale in the Italian saving banking industry. Applied Financial Economics 9, 11—19.

Students are required to write an original thesis that represents the knowledge achieved during the course of her/his study.

Learning goals The student at the end of the course should be able to use with knowledge advanced modeling and exploratory techniques specifically developed for spatially dependent data. This is achieved by assigning several homeworks on real data. Practical sessions with the R software are part of each lecture, so to allow students to implement what is taught in the theoretical part. Among the expected results, ability to elaborate environmental data using R software, ability to interpret the results obtained, ability to choose the most suitable statistical models according to the hypotheses they are founded on and to their compatibility with the data available.
Knowledge and understanding The student will be able to understand the main tools for the analysis of spatial and spatio-temporal data. Also an introductory knowledge of extreme value estimation and modeling will be part of his cultural heritage
Applying knowledge and understanding Students will be involved in the discussion and analysis of case studies using the open source statistical software R. Students will be asked prepare and discuss a presentation of the results of their homeworks. The presentation will be given on front of the class and discussed.
Making judgements Through the homeworks and the final presentations discussions, tudente will develop judgements capacity in terms of theoretical choices in representation of real worls phenomena.
Communication skills Students will be asked prepare and discuss a presentation of the results of their homeworks. The presentation will be given on front of the class and discussed. This procedure will help the student to develop his/her ability to communicate the results of its work.
Learning skills One of the aims of the course is to build a statistical glossary and a dictionary of specific statistical concepts that will allow the student to read and understand scientific papers using advanced statistical tools in the analysis of environmental data.

Gli argomenti del corso sono i seguenti e sono affrontati in lingua inglese:

Introduction to Spatial Statistic

• Exploratory Data Analysis using R
• Non stochastic approaches to spatial interpolation

Geostatistical Models

• Spatial Interpolation under a stochastic model: mixed effect spatial model
• Estimation in the likelihood paradigm
• Bayesian Estimation (with a brief introduction to general Bayesian statistics)

Computational issues in spatial statistics

• The big N problem and some way to deal with it

Lattice Models

• Spatial glm and automodels

Extreme Value theory

• Generalized extreme value distribution
• Genralized Pareto distribution

Spatio temporal modeling of environmental data: some examples

The evaluation of the acquired skills is done through a series of homeworks that will be assigned during classes and posted on the course web page so that they can be developed by not attending students, and a final report based on the homeworks results.

• Spatial Statistics
• Advanced Spatial Statistics
• Extreme value theory

Alla fine del corso si determinerà il voto sulla base degli homework consegnati e di un report finale (individuale) in inglese che riassume le attività svolte negli homewrok.

Slides of the lectures and other teaching material are available online in the DSS elearning web page.

Suggested text books:

• Handbook of Spatial Statistics Editors Alan E. Gelfand, Peter Diggle, Peter Guttorp, Montserrat Fuentes. CRCpress
• Stuart Coles Introduction to Extreme Value theory Springer

A more advanced text books is

Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand Hierarchical Modeling and Analysis for Spatial Data, Second Edition CRCpress

• Handbook of Spatial Statistics Editors Alan E. Gelfand, Peter Diggle, Peter Guttorp, Montserrat Fuentes. CRCpress
• Stuart Coles Introduction to Extreme Value theory Springer
• Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand Hierarchical Modeling and Analysis for Spatial Data, Second Edition CRCpress

General objectives
The primary objective is to study algorithms and data structures that efficiently solve problems whose solution, using trivial approaches, would require very high resources. The algorithms studied will be implemented in Java language.
Specific objectives
Knowledge and ability to understand Basic data structures and their use in solving sorting, searching, and graph problems will be shown. It will be shown how the data structures described are made available in Java language. The student will be able to determine the computational complexity of algorithms, and associate them with the appropriate complexity class.
Ability to apply knowledge and understanding At the end of the course the students will be able to determine algorithms to efficiently solve complex problems, in particular problems on graphs, and choose the most suitable data structures to obtain an efficient implementation of the algorithm. They will also be able to use the Java classes that implement the data structures studied.
Autonomy of judgment Students will be able to distinguish the computational complexity of problems and algorithms, and to identify the computationally more costly steps in solving a problem.
Communication skills Students will be able to describe, in appropriate terms, the characteristics of the main data structures, and identify the primitives needed to efficiently implement an algorithm.
Learning ability Students who pass the exam will be able to take advanced courses, of an applicative nature, that require the use of sophisticated algorithms. They will also be able to appreciate software engineering and computational complexity theory teachings.