Syllabus
I. Classical linear regression model - assumption, inference, diagnostic tools.
II. Econometric problems of linear regression (heteroscedasticity, autocorrelated residuals, dynamic models, instrumental variables).
III. Discrete and limited dependent variables.
IV. Econometric systems of equations: Panel data, SUR model, Simultaneous-equations model
V. Other econometric topics (nonlinear regression, kernel estimation, quantile regression).
Annotation
Overview of modern methods used in econometrics. Econometric problems of linear regression
(heteroscedasticity, autocorrelated residuals, multicollinearity, estimation methods, models with a priori restrictions). Discrete and limited dependent variables. Econometric systems of equations (SUR model, simultaneous-equations model, identification problem, estimation methods). Vector autoregression (causality, response to impulse, cointegration). Requierements: Basic knowledge of statistics.