32626 - ECONOMETRICS

Conoscenze e abilità da conseguire

By the end of the course the student should be familiar with the theory and practice of single-equation linear econometric modelling. In particular, the student should be able to: - specify and estimate linear, single-equation econometric models with stochastic and possibly endogenous regressors; - derive and employ the asymptotic properties of linear method-of moments parameter estimators (OLS and IV) in these models; - perform a specification analysis of these models, - perform asymptotically valid inference based on these models.

Contenuti

1. Introduction to the specification of econometric models.

2. OLS estimation and inference for time series data. Conditions for consistency and asymptotic normality of the OLS estimator.
Tests of linear parametric restrictions under homo and heteroskedasticity.

3. Diagnostic tests.

4. Dealing with endogenous regressors: instrumental variable methods. Introduction to GMM estimation.

A note for exchange students: This is a course for an audience with a strong background in Statistics and with a propensity to discuss the course topics from a mathematically sophisticated perspective. Students from Economics, Business and other degrees who decide to enroll should be aware of the expected profile of the target audience and would enroll at their own risk.

Testi/Bibliografia

Verbeek, M. (2000). An Introduction to Modern Econometrics, Wiley

Versione in italiano:Verbeek (2006) Econometria [testo tradotto in italiano, a cura di S. Pastorello].

Metodi didattici

Theory and empirical classes via Microsoft Teams.

Modalità di verifica dell'apprendimento

At the day of the first exam call that students attend during the academic year, they can make a binding and irreversible choice among two formulas for the calculation of their final course grade:

min{0.25 P + 0.75 E, 31}

and

min{E, 31},

where:

- P is a home assignment grade in [0,32],

- E is a written exam grade in [0,30].

An empirical home assignment will be proposed in the penultimate week of the course and will be due by 24:00h on June 3d, 2020. A different dataset will be sent to each of the students who wish to work on the assignment. Results should be presented in the form of a coherent argumentative text, not in the form of copied and pasted Gretl output tables. Submissions should be made in the pdf format.

The final exam will be a written one and will have two parts: theoretical exercises and questions based on estimation output.

Students are entitled to renounce a passing final course grade one time only.