Statistics and econometrics
A.A. 2023/2024
Obiettivi formativi
Part 1: The aim of this part of the course is to give students the preliminary elements of probability and statistics. This part concentrates on the linear regression model, with particular attention to the OLS estimation procedure and related properties, finite and asymptotic, of the estimators.
Parts 2: The aim of these parts of the course is to provide students with the basic principles of the econometric analysis. All the theoretical aspects of the econometric modelling will be treated jointly with interesting and modern empirical applications in order to motivate students and try to respond to real-world questions with specific numerical answers.
Parts 2: The aim of these parts of the course is to provide students with the basic principles of the econometric analysis. All the theoretical aspects of the econometric modelling will be treated jointly with interesting and modern empirical applications in order to motivate students and try to respond to real-world questions with specific numerical answers.
Risultati apprendimento attesi
At the end of the course the students should be able to formalize autonomously a statistical or econometric empirical problem and perform the related data analysis.
Periodo: Secondo semestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento può essere seguito come corso singolo.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Secondo semestre
Programma
Overview
The agenda includes the linear regression model, heteroskedasticity, instrumental variables, panel data analysis, probit/logit model.
One of the goals is to equip students with working knowledge of the tools of probability and statistics, with skills in data handling and statistical programming, and with an understanding of the models and methods of applied econometrics. To this aim, problem sets with both analytical and computer-exercise components will be a relevant part of the course.
Main topics
0. Basics refresher: probability and random variables.
1. Bayesian inference: models and computation.
2. The linear regression model with a single regressor: estimation through ordinary least squares, reading and interpreting the regression output, using the model for prediction. The linear regression model with multiple regressors.
3. Checking assumptions of the regression model: functional form, multicollinearity, heteroskedasticity, autocorrelation, non-normality;
4. Using the model to investigate causal relationships: omitted variables, endogenous regressors and the method of instrumental variables;
5. Analysis of panel data: fixed effects estimator, random effects estimator, specification tests, Hausman-Taylor estimator;
6. Models for binary dependent variables: probit and logit models.
The agenda includes the linear regression model, heteroskedasticity, instrumental variables, panel data analysis, probit/logit model.
One of the goals is to equip students with working knowledge of the tools of probability and statistics, with skills in data handling and statistical programming, and with an understanding of the models and methods of applied econometrics. To this aim, problem sets with both analytical and computer-exercise components will be a relevant part of the course.
Main topics
0. Basics refresher: probability and random variables.
1. Bayesian inference: models and computation.
2. The linear regression model with a single regressor: estimation through ordinary least squares, reading and interpreting the regression output, using the model for prediction. The linear regression model with multiple regressors.
3. Checking assumptions of the regression model: functional form, multicollinearity, heteroskedasticity, autocorrelation, non-normality;
4. Using the model to investigate causal relationships: omitted variables, endogenous regressors and the method of instrumental variables;
5. Analysis of panel data: fixed effects estimator, random effects estimator, specification tests, Hausman-Taylor estimator;
6. Models for binary dependent variables: probit and logit models.
Prerequisiti
The students should be familiar with basic concepts of matrix algebra and should have attended a basic course in probability and statistics.
Metodi didattici
* Frontal theoretical lectures;
* Practical sessions using R-Studio and Stata (students will be required to solve problem sets in the lab or using their laptops).
* Practical sessions using R-Studio and Stata (students will be required to solve problem sets in the lab or using their laptops).
Materiale di riferimento
* Hill-Griffiths-Lim, Principles of Econometrics, 5th Edition, Wiley
* Greene, Econometric Analysis, 8th Edition, Pearson
* Stock-Watson, Introduction to Econometrics, 4th Edition, Pearson
* Online resources on Bayesian methods
* Greene, Econometric Analysis, 8th Edition, Pearson
* Stock-Watson, Introduction to Econometrics, 4th Edition, Pearson
* Online resources on Bayesian methods
Modalità di verifica dell’apprendimento e criteri di valutazione
The main purpose of the written exam is to assess the achievement of the learning objectives, such as the ability to select the appropriate model to answer research questions, to read the output of econometric softwares, to perfom the appropriate tests, to use statistical models to support economic decisions.
The exam consists of three parts:
* 1st part (statistics): written test with exercises composed of several open questions both theoretical and/or focused on R output to be commented;
* 2nd part (econometrics): written test with two exercises composed of several open questions; the exercises will be theoretical and/or based on an application (for example, Stata output to be commented);
* 3rd part (statistics and econometrics modules combined): NON-mandatory group work with teams of two or three students; each group must present a report on a case study.
The student will pass the exam only if he/she passes both the first and the second part of the exam. The final grade will be unique and given by the average of the scores of the first two parts, plus the additional points (up to 3/30) for the group work.
The exam consists of three parts:
* 1st part (statistics): written test with exercises composed of several open questions both theoretical and/or focused on R output to be commented;
* 2nd part (econometrics): written test with two exercises composed of several open questions; the exercises will be theoretical and/or based on an application (for example, Stata output to be commented);
* 3rd part (statistics and econometrics modules combined): NON-mandatory group work with teams of two or three students; each group must present a report on a case study.
The student will pass the exam only if he/she passes both the first and the second part of the exam. The final grade will be unique and given by the average of the scores of the first two parts, plus the additional points (up to 3/30) for the group work.
SECS-P/05 - ECONOMETRIA - CFU: 3
SECS-S/01 - STATISTICA - CFU: 6
SECS-S/01 - STATISTICA - CFU: 6
Esercitazioni: 16 ore
Lezioni: 64 ore
Lezioni: 64 ore
Siti didattici
Docente/i
Ricevimento:
Giovedì 16:30-18:00 (è necessario richiedere un appuntamento via email)
Microsoft Teams
Ricevimento:
Su appuntamento Martedì e Mercoledì (email)
via Celoria 10