Pooled cross-section models are not the preferred estimators for panel data models because they
A)are heteroskedastic.
B)are quasi-differenced.
C)do nothing to account for the time constant component of the error term.
D)create a dummy variable for each individual in the data set.

Question

Quizplus · Accepted Answer

Pooled cross-section models do not account for the time constant component of the error term, which can affect the accuracy of the estimates. Panel data models, such as random effects or fixed effects models, are the preferred estimators for this reason. Pooled cross-section models are also vulnerable to heteroskedasticity, which can lead to biased standard errors and inconsistent parameter estimates. Quasi-differenced models are sometimes used as an alternative to pooled cross-section models, but they are not as robust as panel data models. Dummy variable regression models are another option for analyzing panel data, but they may not be feasible if the number of individuals in the panel is too large.

Pooled Cross-Section Models Are Not the Preferred Estimators for Panel