Heteroskedasticity and autocorrelation in a random effects model with small sample

[Jonnathas Marques]

Hi
I'm estimating a model for the freight rate as the dependent variable. My model has three explanatory variables, being two invariants in time (continuous) for each cross section and a dummy referring to seasonality. My database has N = 17 and T = 12 (monthly observations). The Chow, LM / Breusch-Pagan and Hausman tests indicated a random effects model. However, I would like to know how to test and treat heteroskedasticity and other problems such as autocorrelation and cross dependency in the context of a random effects model in Stata. Moreover, I wonder if the random effects estimator is adequate and consistent for a sample with small N and T's.

[Phil Bromiley]

You'll increase your chances of a useful answer by following the FAQ on asking questions - provide Stata code in code delimiters, readable Stata output, and sample data using dataex (which you do).

You can always use robust standard errors. Many like xtgls or xtregar for such models.