This is an introductory course to applied research in Economics. Linear econometric techniques together with the required programming skills will be studied. Actual examples of influential studies will be presented, and in some cases, replicated in computer sessions.
Description of contents: programme
Section 1: Data Management and regression: Linear regression model. Models with binary dependent variable. Use and organization of gretl databases. Gretl regression.
Section 2: Instrumental Variables: Endogenous explanatory variables . Consequences on estimation and inference. Valid instruments. Tests of endogeneity and overidentifying restrictions .
Section 3: Pooled Data with Cross Sections. The difference-in-differences estimator. Panel data. First difference estimator. Fixed effects estimator. Random effects estimator.
Learning activities and methodology
The course will consist of three parts:
- Theoretical lectures based on the presentation of influential empirical papers. Reference bibliography will be provided in order to aid the students in delving deeper into the topics they find more interesting.
- Theoretical lectures to teach the students the use of econometric software at an intermediate level. Class notes will be provided.
- Reduced classes in computer classrooms to allow the students to replicate some of the empirical papers presented in class.
The theoretical lessons have the goal of facilitating the understanding of several academic empirical papers. Computer classes aim to give the students the chance of apply the econometric techniques learnt in several courses in order to do empirical work.
% end-of-term-examination 0
% of continuous assessment (assigments, laboratory, practicals...) 100
James H. Stock y Mark M. Watson. Introduction to Econometrics. Pearson Education. 2011
Joshua Angrist and Jörn-Steffen Pischke. Mastering 'Metrics. The Path from Cause to Effect. Princeton University Press. 2014
The course syllabus and the academic weekly planning may change due academic events or other reasons.