Models and techniques for the analysis of cross-sectional data encountered in microeconomics. Binary data, count data, duration and multinomial data models are covered. Analysis of linear and nonlinear panel data with empirical applications and interpretation of final results.
The course will also cover the generalized method of moments estimation techniques. The focus is on applied analysis and replication of published papers.
Wooldridge J.M. (2010) Econometric Analysis of cross-section and panel data, The MIT Press.
Cameron, A.C. and P. K. Trivedi (2005) "MICROECONOMETRICS: Methods and Applications", Cambridge University Press, New York.
Research articles and teaching material provided by the lecturer.
Learning Objectives
The aim of the course is to expose the student to practicalities encountered when doing applied research.
Students will be able to select the most appropriate modelling technique for an array of data types, conduct robustness checks and statistical tests. Students will be able to interpret and discuss the empirical results both in terms of their statistical implications and the implications they have for economic, financial and social theories.
Prerequisites
Econometrics (Macro- and/or Micro-
Econometrics)
Teaching Methods
Hands on approach. Lectures followed by practical examples (reproduction of published papers results)
Further information
Additional material available on the Moodle platform
Type of Assessment
A paper on a topic chosen by the student and the instructor
Course program
First part:
1. Maximum Likelihood: Principles, Properties, Mechanics, Classical Test Principles.
2. Binary Data Models: Link Functions, Interpretation of Coefficients, Latent Variable Models, Likelihood Analysis.
3. Count Data Models: Poisson Regressions, Likelihood Analysis, Over Dispersion: Negative Binomial Types I and II,
4. Duration Data Models: Survival Function, Hazard Rate, Likelihood Analysis.
5. Generalized Method of Moments: Moment Conditions and Identification, Instrumental Variables, MM Estimation, GMM: estimation, consistency, asymptotic distribution, Efficient GMM.
6. Panel Data Models: definition, Fixed and Random Effects estimators, Hasuman test.
Second part:
Implementation of the models studied via the STATA software package. Analysis of research papers. Assignment of topics for individual projects.