Course 2017-2018 a.y.

30413 - ECONOMETRICS


BEMACS
Department of Economics

Course taught in English


Go to class group/s: 25

BEMACS (8 credits - II sem. - OB  |  SECS-P/05)
Course Director:
MASSIMILIANO MARCELLINO

Classes: 25 (II sem.)
Instructors:
Class 25: MASSIMILIANO MARCELLINO


Course Objectives
The course goal is to familiarize students with the theory and use of quantitative methods in economics. The topics of the course are: the linear model and its generalizations; estimation and test theory; econometric specification techniques and model selection problems; instrumental variables, models for qualitative variables and panel data. Such techniques are illustrated both theoretically and by means of empirical economic applications implemented using softwares such as Matlab or R.

Intended Learning Outcomes
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Course Content Summary
  • Introduction to the regression model in the univariate case.
  • The general linear model.
  • Least squares criterion and estimators.
  • Properties of estimators.
  • Tests of linear hypotheses on the parameters of the model.
  • Asymptotic results for the linear model.
  • Generalized least squares.
  • Tests of correct specification.
  • Instrumental variables.
  • Hausman Test.
  • Models for qualitative variables (LPM/Logit/Probit).
  • Tobit model for corner solution responses and censored and truncated regressions.
  • Sample selection corrections.
  • Panel data models.

Each topic is first introduced theoretically and then illustrated through empirical applications.

Teaching methods
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Assessment methods
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Detailed Description of Assessment Methods
L’esame è scritto e può essere sostenuto in un’unica prova generale oppure in due prove parziali (in questo caso il voto finale è la media dei voti delle prove parziali).

Textbooks
  • M. MARCELLINO, Applied Econometrics: An Introduction, Bocconi University Press, 2016.
  • J.M. WOOLDRIGE, Introductory Econometrics: A Modern Approach, South-Western College Pub, 2013, 5th edition.

Prerequisites
Sound knowledge of the topics studied in the courses of mathematics and statistics, including basic programming in Matlab and R.
Last change 13/06/2017 14:59