Statistics and econometrics
A.A. 2020/2021
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 non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Secondo semestre
Teaching methods
Remote teaching will constitute of online lectures using the MS Teams platform. The lectures can be attended both synchronously (based on the usual schedule) and asynchronously as they will be recorded and uploaded on Ariel.
Course syllabus and teaching resources
The contents and reference material will remain unaltered.
Assessment methods and evaluation criteria
In case of remote examination, students will join a MS Teams meeting, will solve the exercises with pen and paper while being proctored through their webcams, and will be required to scan and submit their answers through a smartphone app.
After the publication of the results, professors may decide to summon students for an additional oral examination, in case any irregularity during the written exam is detected. The oral examination will potentially encompass all the topics discussed in the course.
Remote teaching will constitute of online lectures using the MS Teams platform. The lectures can be attended both synchronously (based on the usual schedule) and asynchronously as they will be recorded and uploaded on Ariel.
Course syllabus and teaching resources
The contents and reference material will remain unaltered.
Assessment methods and evaluation criteria
In case of remote examination, students will join a MS Teams meeting, will solve the exercises with pen and paper while being proctored through their webcams, and will be required to scan and submit their answers through a smartphone app.
After the publication of the results, professors may decide to summon students for an additional oral examination, in case any irregularity during the written exam is detected. The oral examination will potentially encompass all the topics discussed in the course.
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 statistics, matrix algebra;
1. The linear regression model with a single regressor: estimation through ordinary least squares, reading and interpreting the regression output, using the model for prediction;
2. 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 statistics, matrix algebra;
1. The linear regression model with a single regressor: estimation through ordinary least squares, reading and interpreting the regression output, using the model for prediction;
2. 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 Stata (students will be required to solve problem sets in the lab or using their laptops).
* Practical sessions using 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
* Stock-Watson, Introduction to Econometrics, 4th Edition, Pearson
* Greene, Econometric Analysis, 8th Edition, Pearson
Lecture notes of the teachers.
* Stock-Watson, Introduction to Econometrics, 4th Edition, Pearson
* Greene, Econometric Analysis, 8th Edition, Pearson
Lecture notes of the teachers.
Modalità di verifica dell’apprendimento e criteri di valutazione
The exam consists of two parts:
* 1st part: written test;
* 2nd part: 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).
The student will pass the exam only if he/she passes both parts. The final grade will be unique and given by the average of the scores of the two parts.
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 the models to support economic decisions.
* 1st part: written test;
* 2nd part: 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).
The student will pass the exam only if he/she passes both parts. The final grade will be unique and given by the average of the scores of the two parts.
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 the models to support economic decisions.
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
Docente/i
Ricevimento:
Giovedì 16:30-18:00 (è necessario richiedere un appuntamento via email)
Microsoft Teams
Ricevimento:
appuntamento per mail