Teaching code

4S02464

Teacher

Alessandro Bucciol

Coordinatore

Alessandro Bucciol

Credits

9

Language

English

Scientific Disciplinary Sector (SSD)

SECS-P/05 - ECONOMETRICS

Period

secondo semestre (lauree magistrali) dal Mar 1, 2021 al Jun 1, 2021.

Lessons timetable Moodle Seminars

Learning outcomes

The course provides an overview of the main econometric tools, with particular emphasis on economic applications, developed interactively using professional packages. The program covers standard econometric models (OLS regression and its diagnostics) as well as more advanced models for the analysis of cross-sectional, time series and panel data (IV, probit, tobit, random and fixed effects). Particular attention will be given to the intuition behind each topic, in addition to more formal issues. Towards the end of the course a voluntary assignment will be proposed, with the aim of translating research questions into empirical analyses, applying on real data the tools learnt in class, and stimulating discussion among students. At the end of the course, students should be able to: i) read and critically interpret empirical works developed by other researchers, ii) manage small and large datasets in order to extract useful information, and iii) design and implement on their own empirical analyses based on real data.

Program

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

2) Ordinary Least Squares (OLS) Estimator
2.1) Introduction
Univariate and multivariate regression; marginal effects and elasticity; R-squared and adjusted R-squared; outliers.
2.2) Properties
Gauss-Markov assumptions; unbiasedness; efficiency; consistency; asymptotic normality.
2.3) 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) Heteroskedasticity
White test and Breusch-Pagan test; White robust standard errors.
3.3) Time series
Durbin-Watson test and Breusch-Godfrey test; Newey-West robust standard errors.

4) Instrumental Variable (IV) Estimator
4.1) Estimator
Assumptions; Simple instrumental variable (SIV) and generalized instrumental variable (GIV); properties; two-stage derivation (2SLS).
4.2) Instrument selection
Relevance test; weak instruments; Sargan validity test; Hausman exogeneity test.

5) Limited Dependent Variable (LDV)
5.1) Binary dependent variable
Linear probability model (LPM); probit and logit models; marginal effects; maximum likelihood estimate; goodness of fit; hypothesis testing.
5.2) Truncated and censored data
Truncated regression; tobit-I model; tobit-II and heckman models; marginal effects; goodness of fit.

6) Models for Panel Data
6.1) Main models
Pooled effects, fixed effects and random effects; goodness of fit.
6.2) Tests and further models
Comparison tests; attrition; diff-in-diff; dynamic models.

Examination Methods

The exam is made of a written essay and a homework. Both are mandatory. The final grade is the average between the grades of the essay and the homework, weighted 75% and 25% respectively. In order to pass the exam, it is mandatory to get a grade not below 16/30 in the written essay.