Econometric Modeling (in English)

Indicative content

1. Random variable and its distribution, classical linear regression model, least squares method
and matrix algebra.
2. Statistical properties of small samples and least squares method, unbiasedness, efficiency,
hypothesis testing, linear hypotheses.
3. Maximum likelihood method, Cramer-Rao theorem, information matrix.
4. Testing of nonlinear hypotheses, Wald test, Lagrange multiplier test, likelihood ratio test, delta
method.
5. Estimation of models with restrictions and nonlinear models, Gauss and Newton method,
Newton and Raphson method.
6. Generalized least squares method, spherical and non-spherical stochastic term.
7. Heteroskedasticity and autocorrelation robust estimators, White estimator and Newey and West estimator. 8. Introduction to asymptotic theory, endogenous explanatory variables, instrumental variables, introduction to the method of moments. 9. Generalized method of moments and estimation of forward-looking models. 10. Applications of the generalized method of moments. 11. Dynamic models, models with polynomial distributed lag, dynamic multipliers and impulse response functions. 12. The meaning of regression and its use in econometrics, experiments in econometrics. 13. Panel data, methods of estimation for the models with fixed or random effects.