Course Content
WeekTopicsStudy Materials _ocw_rs_drs_yontem
1 Model specification, model specifation error and types of model specification error Readings and problem set
2 Tests of model specification error , errors of measurement and model selection criteria. Readings and problem set
3 Dynamanic models: Estimation of Distributed -Lag models. The Koyck Approach, Adaptive expectation, Partial adjustment Readings and problem set
4 Estimation of Autoregressive models: Almon approach, polinominal spline distrubuted lags, exogeneity tests, the wu-hausman test Readings and problem set
5 Estimation of Autoregressive models: Almon approach, polinominal spline distrubuted lags, exogeneity tests, the wu-hausman test Readings and problem set
6 Simultaneous equation models:specifation and identification in Simultaneous equation models Readings and problem set
7 Maximum likelihood (ML): likelihood ratio test, wald test, lagrange multiplier tests Readings and problem set
8 Midterm
9 Maximum likelihood (ML): likelihood ratio test, wald test, lagrange multiplier tests Readings and problem set
10 Discrete choice models: Linear probability model, probit and logit models Readings and problem set
11 Limited dependent variables: truncating, censoring, and sample selection Readings and problem set
12 Tobit model, Heckman model Readings and problem set
13 Panel data analysis: general terms of panel data, advantages of panel data, pooled least squares Readings and problem set
14 panel data analysis fixed effects models and random effects models Readings and problem set
15 Time series modeling: ARMA processes, testing and estimation for stationarity, forecast Readings and problem set
16-17 Final Exam

Recommended or Required Reading
Textbook
Additional Resources