1. Econometric Analysis of Panel Data Panel Data Analysis – Linear Model One-Way Effects Two-Way Effects – Pooled Regression Classical Model Extensions

2. Panel Data Analysis Linear Model Representation

4. Linear Panel Data Model (1) One-Way (Individual) Effects

5. Linear Panel Data Model (1) One-Way (Time) Effects

6. Linear Panel Data Model (1) Two-Way Effects

7. Linear Panel Data Model (2) One-Way (Individual) Effects

8. Linear Panel Data Model (2) One-Way (Time) Effects

9. Linear Panel Data Model (2) Two-Way Effects

10. Panel Data Analysis Between Estimator If, then the pooled or population-averaged model is more efficient.

11. Panel Data Analysis Linear Pooled (Constant Effects) Model

12. Pooled Regression Model Classical Assumptions – Strict Exogeneity – Homoschedasticity – No cross section and time series correlation

13. Pooled Regression Model Extensions – Weak Exogeneity – Heteroschedasticity

14. Pooled Regression Model Extensions – Time Series Correlation (with cross section independence for short panels)

15. Pooled Regression Model Extensions – Cross Section Correlation (with time series independence for long panels)

16. Pooled Regression Model Extensions – Cross Section and Time Series Correlation

17. Alternative Pool Regression Models Between (Group Means) Estimator First-Difference Estimator Within (Group Mean Deviations) Estimator

18. Pooled Regression: OLS Classical Model Estimation (OLS) Variance estimator is inconsistent because of heteroscedasticity and autocorrelation.

19. Pooled Regression: OLS Panel-Robust Variance-Covariance Matrix – Adjusting general heteroscedasticity and serial correlation within panel

20. Pooled Regression: GLS The Model Generalized Least Squares (GLS) – If cross sections are independent (short panels) – where is the consistent estimator of

21. Pooled Regression: GLS Heteroscedasticity Cross Section Correlation Time Series Correlation

22. Pooled Regression: GLS Examples of Time Series Correlation – Equal-Correlation – AR(1) – Stationary(1) – Nonstationary(1)

23. Model Extensions Time-invariant regressors Random regressors Lagged dependent variables Dynamic models

24. Example: Investment Demand Grunfeld and Griliches [1960] – i = 10 firms: GM, CH, GE, WE, US, AF, DM, GY, UN, IBM; t = 20 years: 1935-1954 – I it = Gross investment – F it = Market value – C it = Value of the stock of plant and equipment

25. Example: Investment Demand Pooled Model (Population-Averaged Model) Classical OLS Panel-Robust OLS Feasible GLS – Heteroscedastcity – Autocorrelation