Econometrics (2016/2017)

Course code
Name of lecturer
Alessandro Bucciol
Alessandro Bucciol
Number of ECTS credits allocated
Disciplinary sector
Language of instruction
Primo semestre Magistrali dal Sep 26, 2016 al Jan 13, 2017.

Lesson timetable

Primo semestre Magistrali
Day Time Type Place Note
Monday 2:00 PM - 4:15 PM lesson Laboratory LAB.SMS.3 - Aula Informatica  
Wednesday 2:00 PM - 4:15 PM lesson Laboratory LAB.SMS.3 - Aula Informatica  

Learning outcomes

The course provides an overview of the main econometric tools, with particular emphasis on economic applications, developed interactively in class using the professional software Stata™.
After a short introduction on the purpose of Econometrics and the basic commands in Stata, the program is divided in four parts. The first part (OLS) introduces to the standard econometric method, i.e., ordinary least squares (OLS) regression. The second part (OLS diagnostics) presents diagnostic tests on heteroskedasticity, autocorrelation and wrong specification of the functional form. The third part (IV) discusses the problem of endogeneity and the instrumental variable estimators. The fourth part (extensions) introduces micro-econometric models suited for panel data (random effects, fixed effects), for binary dependent variables (probit, logit), and for limited dependent variables (truncated regression, tobit).


1) Introduction
1.1) What is Econometrics?
Definition; cross-section, time series and panel data.
1.2) Stata tutorial
Data management; basic statistics; graphics.

2) Ordinary Least Squares (OLS) Estimator
2.1) Introduction
Univariate and multivariate regression; marginal effects and elasticity.
2.2) Goodness of fit
R2, adjusted R2, AIC and BIC criteria; forecast; outliers.
2.3) Properties
Gauss-Markov assumptions; unbiasedness; efficiency; consistency; asymptotic normality.
2.4) Testing
t-test on one restriction; F test on several restrictions.

3) OLS Diagnostics
3.1) Specification
Collinearity; superfluous and omitted variables; RESET test of specification; Chow test of structural stability.
3.2) Heteroscedasticity
White test and Breusch-Pagan test; White robust standard errors.
3.3) Autocorrelation
Durbin-Watson test and Breusch-Godfrey test; Newey-West robust standard errors.

4) Instrumental Variable (IV) Estimator
4.1) Motivation
Autocorrelation and lagged dependent variable; measurement error; omitted variables; simultaneity.
4.2) Estimator
Assumptions; Simple instrumental variable (SIV) and generalized instrumental variable (GIV); properties; two-stage derivation (2SLS).
4.3) Instrument selection
Relevance test; weak instruments; Sargan validity test; Hausman exogeneity test.

5) Extensions (Microeconometrics)
5.1) Panel data
Pooled effects, fixed effects and random effects; goodness of fit; comparison tests.
5.2) Binary dependent variable
Linear probability model (LPM); probit and logit models; marginal effects; maximum likelihood estimate; goodness of fit; hypothesis testing.
5.3) Limited dependent variable
Truncated regression; Tobit models; marginal effects; hypothesis testing.

Suggested material:
- Course slides, available on eLearning.
- Verbeek, M., A Guide to Modern Econometrics, Wiley, 2000 or following editions.

Reference books
Author Title Publisher Year ISBN Note
Verbeek A Guide to Modern Econometrics John Wiley and Sons 2012 1119951674

Assessment methods and criteria

The exam is written. The final grade is based on one mandatory final exam and one voluntary homework (assigned during the semester).
The final exam includes theoretical, numerical and applied exercises on all the topics covered in class; the homework includes applied exercises. Applied exercises require the use of Stata.
During the final exam it will be allowed the use of handheld calculators, but not the use of textbooks or teaching notes.
The homework adds 1 bonus point to the final grade and accounts for 10% of the final grade.

Statistics about transparency requirements (Attuazione Art. 2 del D.M. 31/10/2007, n. 544)

Outcomes Exams Outcomes Percentages Average Standard Deviation
Positive 44.0% 27 2
Rejected 28.0%
Absent 24.0%
Ritirati 4.0%
Canceled --
Distribuzione degli esiti positivi
18 19 20 21 22 23 24 25 26 27 28 29 30 30 e Lode
0.0% 0.0% 0.0% 0.0% 9.0% 0.0% 0.0% 18.1% 0.0% 9.0% 18.1% 27.2% 9.0% 9.0%

Data from AA 2016/2017 based on 25 students. I valori in percentuale sono arrotondati al numero intero più vicino.