What could be causing errors when estimating coefficients with xtgls in stata for unbalanced panel data over 4 years?

1
votes

I am using unbalanced panel data for 4 years. In trying to decide which time variant model (xtgls, xtreg, re, or xtgee) is most appropriate for my analysis, I am trying to estimate coefficients for xtgls under both the homoskedasticity and hetero assumptions. When I run this model with the hetero option, I get very high z-scores (>30) and a significant effect on a term that is insig in all other models.

Also, when I attempt to run lrtest comparing the hetero and homoskedastic models I get an error that reads “hetero does not contain scalar e(ll)”. I read that one way to address this is to add option igls, which supposedly gives the same coeff as the model without the igls option. However, my model will not converge with the igls option. I thought these odd results for the hetero xtgls model could be because some time invariant variable was miscoded (i.e. person coded as female = 1 for one year and female = 0 for another year). I checked my 2 ivs and this is not the case. I can’t figure out what else could be causing this.

So my specific questions are:

  1. Why would I be getting this error - “hetero does not contain scalar e(ll)” - for the lrtest comparing the homo and hetero models? What does it mean? Below is my stata code:

xtgls continuous_DV IV1 IV2 IV1xIV2, i(person_id) panels(hetero)

estimates store hetero

xtgls continuous_DV IV1 IV2 IV1xIV2, i(person_id)

local df=e(N_g)-1

disp `df'

lrtest hetero ., df(`df')

I ran xttest3 which indicated errors are hetero.

  1. Is igls an appropriate work around for the error I am getting following the lrtest (“hetero does not contain scalar e(ll)”)? If so, what could be causing this model with the igls option not to converge? Below is the code:

xtgls continuous_DV IV1 IV2 IV1xIV2, i(person_id) panels(hetero) igls

1
stata

1 Answers

1
votes

In Stata, the xtgls command does not estimate a log likelihood because it is not maximum likelihood estimation. So you cannot get a log-likelihood test out of that model. To get a log-likelihood, you need to use the setup you had above but instead use the igls option. That is an appropriate workaround and is entirely appropriate; I don't think you need to start by slashing your dataset.

Alternatively, you can use a different estimator. GLS is appropriate when you have few, wide panels. If you have really short panels (only a couple years per individual), you should probably use something like xtreg. http://www.stata.com/support/faqs/statistics/xtgls-versus-regress/