I have panel data which includes American states (1-48) and years (1900-1917). All the variables are time-varying with one exception. This exception is time invariant and a three level categorical variable measuring regional designations for the states tested using two dummy variables. I also want look at interactions between one of the dummies and several of the time-varying variables. OLS estimates of this model indicate heteroscedasticity. So given the organization of the data my question is which of the panel techniques is best and why? I am using Stata. Thanks, RB
I would say it depends on the data. But there are some general ideas about model specification. Fixed effects method is consistent and therefore should be used to control subject-level confounders. If there is no subject-level confounders, random effects method is efficient to account for correlated errors.
- OLS vs. Fixed effects: F-test of the joint significance of the fixed effects intercepts. The null hypothesis is that all of the fixed effect intercepts are zero. If the null is rejected, then we need to use fixed effects method. The F-test is automatically conducted when we run
Stata. It appears at the bottom of regression output.
- Fixed vs. random effects: Hausman test. The null hypothesis is that the slope coefficients of the two models being compared do not differ significantly. If the estimates are different then we reject romdom effects and must use fixed effects, otherwise we use the more efficient random effects method. There is a command