Dear Stata users,
I'm dealing with an unbalanced panel dataset of many individuals over two years, my purpose is to study the determinants of italian households financial planning.
I find a paper of Mithcell A.Petersen called "Estimating Standard errors in Finance Panel Data Sets: Comparing approaches" whose has radically changed my point of view in the method of study.
In fact before I runned a Breusch-Pagan LM test for random effects versus OLS, choosing the second.
After I runned an Hausman test for fixed versus random effects model, this time was most suitable a fixed effects model for my data.
The paper by Petersen (2009) change my mind, in fact He didn't consider this approach (very simple) but instead He carried about the unbiasedness of the standard errors to choose the best method to apply for a research dealing with panel dataset. He distinguished between two general forms of dependence:
1) "The residuals of a given firm may be correlated across years for a given firm (time series dependence), unobserved effect firm"
2) "The residuals of a given year may be correlated across different firms (cross-sectional dependence), time effect"

He tested various model used in the literature towards various simulation (considering only one effect by time or togheter) and different fractions of variance in the independent variable and in the residuals that is due by this two effects.
Finally he proposed this approach:
1) Run an OLS with white standard errors
2) Run an OLS with standard errors clusterd by firm
3) Run an OLS with standard errors clusterd by year
4)Run an OlS with standard errors clusterd by both

If the second model has larger (3 / 4 times) standard errors than the first model, there is a firm effect and standard errors clustered by firm produce unbiased standard errors.
If the third model has larger (3 /4 times) standard errors than the first model there is a time effect and the Fama-MacBeth model is the best.
If the fourth model has larget (3 / 4 times) standard errors than the second and third model, cluster by firms and year.

I understood the reasoning behing this approach, but when I try to replicate it on STATA I can't because with the command "xtreg.....vce (cluster year)" I can't estimate the third case beacuse it returns:
"option cluster() not allowed" , the wierd thing is that with a routine provided by professor Petersen I can cluster both for each dimension, but not for only for time.

Can anyone help me, please?