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	Syllabus Econometrics

This plan is preliminary, it may be subject to revision during the course.

1. Recap and exposition of matrix algebra, projection matrices, quadratic forms.
2. Covariance matrices, conditional expectations, the law of iterated expectations. Linear regression as a projection. Method of Moments Estimator (MME.)
3. OLS (Ordinary Least Squares) in the simplest case; homo­skedastic resituals, BLUES, standard errors of estimated parameters, R².
4. Erroneous model specifications:
   • omitted regressors
   • (self) selection bias
   • simultaniety
   • measurement errors in regressors
   • heteroskedasticity
   • multicolinearity
   • non-linearity
   • censored data
5. White's hetero­skedasticity consistent estimator
6. GLS (Generalised Least Squares)
7. Instru­mental variables and 2SLS (Two-Stage Least Squares)
8. Linear restric­tions, test of linear restric­tions: the Wald test. Predic­tion, predic­tion errors
9. NLLS; Non-Linear Least Squares. INLLS, i.e., NLLS with instrumental variables (not in Hansen)
10. Least Absolute Deviation (LAD) Regression and Quantile Regression
11. Boot­strapping methods
12. Truncated dependent variables: censorerd data, binary choise, Maxumum Likelihood Estimator (MLE,) Logit and Probit models.

Sections in Hansen's text, jan. 2008 revision

Ch. 1 Appendix A except A10 Ch. 2 Ch. 3 except 3.7, 3.10 and 3.12; Appendix C Ch. 4 except 4.5, 4.12, 4.13 and 4.15 Ch. 5 except 5.7 Ch. 6 in part Ch. 7 except 7.6–7.9 Ch. 9 except 9.6 Ch. 12