B000221 - ECONOMETRICS

English

This course develops models and techniques for analysing cross-sectional and panel data encountered in microeconometric analysis. Binary data, count data, duration, panel data and multinomial data models are covered. The course will also cover maximum likelihood, quasi-maximum likelihood and the generalized method of moments estimation techniques: used to estimate the parameters of the models’ covered. The focus is on applied analysis.

Cameron, A.C. and P. K. Trivedi (2005) "MICROECONOMETRICS: Methods and Applications", Cambridge University Press, New York.

At the end of the course, students will be able to select the most appropriate modelling to technique for an array of data types. They will also be able to comfortably select the best model specification, 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.

Econometrics.

Lectures and walk-through simple empirical case studies.

None.

Written exam consisting of:
- theoretical questions requiring derivations and/or a few computations
- empirical component requiring the use of some statistical package to carry out the estimation and analysis of simple case studies

1. Introduction: Monte Carlo Simulations, Law of Large Numbers, Unbiasedness and Central Limit Theorem.
2. Maximum Likelihood: Principles, Properties, Mechanics, Classical Test Principles: Wald, Likelihood Ratio, Lagrange, Examples: Binomial Trials & Linear Regressions.
3. Binary Data Models: Linear Regressions, Link Functions, Interpretation of Coefficients, Latent Variable Models, Likelihood Analysis, Goodness of fit, Example: Teaching Economics.
4. Count Data Models: Linear Regressions, Poisson Regressions, Likelihood Analysis, Over Dispersion: Negative Binomial Types I and II, Example: A Model for Arrests.
5. Duration Data Models: Survival Function, Hazard Rate, Likelihood Analysis, Censoring, Example: Labor Strikes.
6. Quasi-Maximum Likelihood: Maximum Likelihood Issues, Quasi-Maximum Likelihood, Properties, Example: Linear Exponential Densities.
7. Generalized Method of Moments: Moment Conditions and Identification, Instrumental Variables, MM Estimation, GMM: estimation, consistency, asymptotic distribution, Efficient GMM, Comparison with Maximum Likelihood, Examples: Mean, OLS, IV, 2SLS and C-CAPM.
8. Panel Data Models: Data Example, Pooled OLS, Unobserved Heterogeneity, Fixed Effects, Random Effects, Hausman Test.
9. Multinomial Data Models: Multinomial Logit, Nested Logit, Multinomial Probit, Example: Delisting of Public Companies.