ü Why Panel Data?
Advantages of Panel Data
1. They increase the sample size considerably.
2. By studying repeated cross-section observations, panel data are better suited to study the dynamics of change.
3. Panel data enable us to study more complicated behavioral models.
Balanced Panel – panel data in which each cross-sectional unit has the same number of time series observations.
Unbalanced Panel – panel data in which the number of observations differs among panel members.
ü Estimation of Panel Data Regression Models: The Fixed Effects Approach
1. All coefficients constant across time and individuals.
2. Slope coefficients constant but the intercept varies across individuals: The fixed effects or least-squares dummy variable (LSDV) regression model.
3. Slope coefficients constant but the intercept varies over individuals as well as time.
4. All coefficients vary across individuals.
ü A Caution in the Use of the Fixed Effects, or LSDV, Model
1. If you introduce too many dummy variables, you will run up against the degrees of freedom problem.
2. With so many variables in the model, there is always the possibility of multicollinearity, which might make precise estimation of one or more parameters difficult.
3. Suppose in the FEM we also include variables such as sex, color and ethnicity, which are time invariant too because an individual’s sex, color or ethnicity does not change over time.
4. We have to think carefully about the error term, uit.
ü Estimation of Panel Data Regression Models: The Random Effects Approach
ü Panel Data Regressors: Some Concluding Comments
1. Hypothesis testing with panel data.
2. Heteroscedasticity and autocorrelation in EAU.
3. Unbalanced panel data.
4. Dynamic panel data models in which the lagged values of the regressand (Yi) appears as an explanatory variable.
5. Simultaneous equations involving panel data.
6. Qualitative dependent variables and panel data.
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