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st: panel data analysis using xtregar

From   Julius Frédéric André <>
To   <>
Subject   st: panel data analysis using xtregar
Date   Sat, 26 Nov 2005 19:26:29 +0100

Dear statalist,

Currently researching balanced panel data using insolvency prediction
estimates for different companies and regressing them (among other
controlling, time-variant variables) on time dummies to evaluate the effects
of a policy introduction in a certain year. I am using xtreg, fe for
evaluation, the panel is described by t=12 and i=110. I presume
autocorrelation of the insolvency predictor (ie. the dependent variable) and
therefore constructed a dataset with a lagged variable for the insolvency
predictor, hence also had to eliminate the first period of the original
dataset. It turned out that the estimator of the lagged variable is indeed
highly significant.

Now I also consider using xtregar. Being new to stata (also to panel data
and time series analysis), however, I unfortunately could not find the
underlying formula used by Stata up to now. Would such an autoregressive
model be similar to the approach I did manually with introducing a lagged
variable and deleting the values of the first period for each I? The stata
result being different for the AR(1) model and the fixed-effect model
including a lagged variable do not corroborate this assumption, so I assume
some other underlying model.  I know that an AR appproach incorporates past
values, but does this mean the past error term, the past dependent or the
past independent variable (or all of them)? It would be of great help if
someone could provide a formula here or give a brief statement if I should
resort to xtregar at all if I assume autocorrelation anyways (so that the
applying AR is simply not necessary anymore).

Additionally, I would like to backup my decision to include a lagged
variable in the model by testing on autocorrelation, using a Durbin-Watson
substitute for panel data, such as Bhargava et al. I know that Stata can
calculate this statistic when using xtregar, however, it seems to me that
the reported output is the result AFTER applying the AR (1) model,
indicating only the presence or absence (or degree) of autocorrelation left
after using the AR model? Is this correct, or does the test statistic
indicate autocorrelation before using the AR model?

Lastly, I am not sure whether I could also use the Baltagi-Wu test score
alternatively, seeing to it that I have a balanced dataset. Is this test
statistic only working with unbalanced data?

The three preceding issues certainly are still on a somewhat basic level,
however, I would definitely appreciate useful comments on any or all of

Thank you very much in advance,


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