79062 - Econometrics

Course Unit Page

  • Teacher Andrea Carriero

  • Credits 6

  • SSD SECS-P/05

  • Language English

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Statistical Sciences (cod. 9222)

    Also valid for First cycle degree programme (L) in Statistical Sciences (cod. 8873)

Academic Year 2021/2022

Learning outcomes

By the end of the course the student should have acquired the basics of econometric modelling. In particular the student should be able: - to specify and estimate linear, single-equation econometric models and to face the endogenous regressors issue; - to perform a specification analysis of the model

Course contents

  1. The Classical Linear Regression Model. Derivation of Ordinary Least Squares estimator (OLS). Decomposition of variance, R-squared.
  2. Small sample properties of the OLS estimator. Gauss-Markov Theorem
  3. Partitioned Regression, redundant/omitted variables, bias-variance trade-off, Frisch Waugh Theorem
  4. Inference. Tests of simple and joint hypothesis. Restricted Least Squares (RLS).
  5. Heteroscedasticity and autocorrelation. Generalised Linear Regression Model. Generalised Least squares Estimator (GLS), Feasible GLS (FGLS), HAC estimators.
  6. Stochastic regressors. Endogeneity. Large sample properties of OLS estimator.
  7. Instrumental Variables estimator (IV). Generalised IV (GIVE) and Two-Stage Least Squares estimator (TSLS).
  8. Maximum Likelihood Estimation (ML).
  9. Bayesian analysis of the linear regression model.




Greene “Econometrics Analysis”, Pearson – any edition

Hansen “Econometrics” – manuscript, any edition

Assessment methods

Written examination. The exam will be bases on closed-book questions which on the material covered in the lectures. 

Office hours

See the website of Andrea Carriero