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long time series (over 20-30 years). This is not much of a problem in micro panels (few years and large number of cases). The null hypothesis in the B-P/LM test of independence is that residuals across entities are not correlated. The command to run this test is xttest2 (run it after xtreg, fe):

xtreg top1 demcont repcont top1lag, fe

xttest2

If the number to the right of “Pr” is less than .05 reject the null hypothesis that residuals across entities are independent (i.e., uncorrelated).  When I ran the test above, I received an error message that read: “too few common observations across panel.  no observations”.  Rejection of the null hypothesis could lead to using the “robust” standard errors model shown below.  Also, see next test discussed.

5. As mentioned above, cross-sectional dependence is more of an issue in

macro panels with long time series (over 20-30 years) than in micro panels. Pasaran CD (cross-sectional dependence) test is used to test whether the residuals are correlated across entities. Cross-sectional dependence can lead to bias in tests results (also called contemporaneous correlation). The null hypothesis is that residuals are not correlated.

xtreg top1 demcont repcont top1lag, fe

xtcsd, pesaran abs

If the number to the right of “Pr” is less than .05 reject the null hypothesis that the residuals are not correlated. When I ran this test I received the following error message: “The panel is highly unbalanced. Not enough common observations across panel to perform Pesaran's test. insufficient observations”. Had cross-sectional dependence be present Hoechle suggests to use Driscoll and Kraay standard errors using the command xtscc.

xtscc top1 demcont repcont top1lag, fe  

Note: even though I received the error message above when I ran the xtcsd test, I did not receive an error message when I executed the xtscc command.

Possible Models:

1.The model with heteroscedasticity:

xtreg top1 demcont repcont top1lag, fe vce(robust)

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