Teaching code

4S02464

Teacher

Alessandro Bucciol

Coordinator

Alessandro Bucciol

Credits

9

Language

English

Scientific Disciplinary Sector (SSD)

SECS-P/05 - ECONOMETRICS

Period

Secondo semestre (lauree magistrali) dal Feb 26, 2024 al May 24, 2024.

Courses Single

Authorized

Lessons timetable Moodle Seminars 0

Learning objectives

The course provides an overview of the main econometric tools, with particular emphasis on economic applications, developed interactively using the R package. The program covers standard econometric models (OLS regression and its diagnostics) as well as more advanced models for the analysis of cross-sectional (IV, probit, tobit), time series (autoregressive) and panel data (random and fixed effects). Particular attention will be given to the intuition behind each topic, in addition to more formal issues.
Part of the evaluation is based on the delivery of an assignment, whose aim is applying on concrete cases 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.

Prerequisites and basic notions

Working knowledge of basic mathematics and statistics is recommended.

Program

1. INTRODUCTION
1a. What is Econometrics?
Definition; cross-section, time series and panel data.
1b. R tutorial
Data management; basic statistics; graphics.
2. ORDINARY LEAST SQUARES (OLS) ESTIMATOR
2a. Introduction
Univariate and multivariate regression; marginal effects and elasticity; R-squared and adjusted R-squared; outliers.
2b. Properties
Gauss-Markov assumptions; unbiasedness; efficiency; consistency; asymptotic normality.
2c. Testing
t-test on one restriction; F test on several restrictions.
3. OLS DIAGNOSTICS
3a. Specification
Collinearity; superfluous and omitted variables; RESET test of specification; Chow test of structural stability.
3b. Heteroskedasticity
White test and Breusch-Pagan test; White robust standard errors.
3c. Time series
Stationarity; AR processes; Durbin-Watson and Breusch-Godfrey tests; Newey-West robust standard errors.
4. INSTRUMENTAL VARIABILE (IV) ESTIMATOR
4a. Estimator
Assumptions; Simple instrumental variable (SIV) and generalized instrumental variable (GIV); properties; two-stage derivation (2SLS).
4b. Instrument selection
Relevance test; weak instruments; Sargan validity test; Hausman exogeneity test.
5. LIMITED DEPENDENT VARIABLE (LDV)
5a. Binary dependent variable
Linear probability model (LPM); probit and logit models; marginal effects; maximum likelihood estimate; goodness of fit; hypothesis testing.
5b. Truncated and censored data
Truncated regression; tobit-I model; tobit-II and heckman models; marginal effects; goodness of fit.
6. PANEL DATA
6a. Models
Difference-in-difference; Pooled effects, fixed effects and random effects.
6b. Testing
Goodness of fit; comparison tests; attrition.

Bibliography

Didactic methods

Frontal teaching. Lectures include real examples interactively developed using the data management free software R.

Learning assessment procedures

The exam is made of one written essay and one individual homework. No oral integration is planned.
The written essay lasts one hour and thirty minutes and covers the whole program of the course. Use of handheld calculators is allowed, but use of personal notes or other teaching material is not allowed.
The homework is developed individually, and can be of two types (Homework I or Homework II). Homework I aims to develop analytical skills through personal data analysis. Homework II aims to develop critical skills with respect to empirical applications. Each student can choose which type of homework to deliver, but must deliver one of them. Once the deadline for delivery of Homework I has expired, it is possible to deliver Homework II only. The homework has to be delivered before taking part in the written essay; its grade remains valid throughout the academic year.

Evaluation criteria

The written essay evaluates general understanding of the main econometric topics and the ability to understand and interpret tables reporting econometric output. The homeworks ascertain the ability to develop empirical research through personal elaboration (Homework I) or to critically comment empirical research developed by others (Homework II).

Criteria for the composition of the final grade

The final grade is given by the average of the grades in the essay and the homework, with 80% and 20% weights respectively. In order to pass the exam, it is necessary to obtain a grade not below 18/30 in the written essay. Students can separately reject the essay grade and the homework grade. However, the homework grade can be rejected only once.

Exam language

Inglese