Hello,

I am trying to estimate the effect of state law on number of crashes relative to total vehicle miles traveled. To assess the effect of implementation of the law, I created a dummy variable law (1=presence of the law; 0=absence of the law). My unit of analysis is state and I have pooled cross-sectional time-series data. My N=50 and My T=29 (1985-2014). I have about 1500 observations. It appears that there is a significant auto-correlation in the dataset based on the Wooldridge test (p<.000), hereoskedasticity, and contemporaneous correlation. Data are stationary.

Here are the models that I consider:
Code:
xtgls crashes_r_vtm l.law year Alabama - Wyoming ,  panels (heteroskedastic) corr(ar1) force
Code:
xtreg crashes_r_vtm l.law year  , fe cluster (state)
Code:
xtregar crashes_r_vtm l.law   , fe
Would you please help me to decide what command is the best for my N and T. I have read some previous discussions that if N>T, xtreg is more appropriate. Would be that the case in my data even if my T=29? The results for xtgls and xtreg for dummy variable l.law are significant, but they are not significant for xtregar.
How can I decide if I should use xtregar?
I read on forum that
"xtregar- are recommended whenever you have a T>N panel data structure, when the autocorrelation preocess is AR1 (something unfeasible with -xtreg-)." Would that mean that I should choose xtreg with cluster errors? (again there is significant autocorrelation).


Finally, my second variable is drug crashes relative to total vehicle traveled. This measure is highly positively skewed. Should I use a log term of this variable?
Thank you so much for your help.

Sincerely,
Sylwia