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Hello,

I am working on an unbalanced panel data (T=17 and N=225). A sample would look like ;

FirmID Year Industry Y Var1 Var2
xyz 1997 Automobile 10 12 6
xyz 1998 Automobile 11 13 1
xyz 1999 Automobile 19 4 8
zzz 2003 Utilities 5 3 7
zzz 2004 Utilities 7 9 4



I have basically followed the guidance from this document : http://www.econ.canterbury.ac.nz/person ... _Week3.pdf
Despite the fact that T<N I estimated a cross sectional Fixed effects model for theoretical reasons. My panel being unbalanced I am unable to use the SUR options when selecting the GLS weighting. As far as Cross Section Weights are concerned, I get the following error message "Positive or non negative argument to function expected in computation of group weight (variance)". So I am not using GLS weights,
Therefore, I am mainly preoccupied with the standard erros and covraiance adjustements. I am not sure of the option I should pick among White cross-section, white period or diagonal. After some reading, I understood that :

-White cross-section method assumes that the errors are contemporaneously correlated (period clustered). It is cross-sectional dependant robust
-White period assumes that the errors for a cross section are heterosckedastic and serially correlated (cross section cluster). The estimator is designed to accommodate arbitrary heteroskedasticity and within cross section serial correlation
-The white diagonal method is robust to observation specific heteroskedasticity in the disturbances but not to correlation between residuals for different observations.

So far I have noticed that using the white diagonal leads to way higher SE's, which in terms of inference lead to accept the significance of almost all explanatory variables of my database.

I would pick white cross section (I believe it makes more sense considering my data) and in ordrer to control for serial correlation (having so far a low DW stat), I would add the lagged dependent variable to my specification.

1/ Is that a good approach?

2/ Is there any way in EViews to specificy that the cluster I have in mind is "Industry" rather than Firm ID when computing the coeff covariance method without restructuring the whole database ? Alternatively I tried to include dummy for Industries in the equation (10 industries, so I added 10 - 1=9 dummies but I get the "Near Singular Matrix" error)

Thanks a lot for your help :D

PS/ I already went through most of the topics in this forum about the subject ( such as http://forums.eviews.com/viewtopic.php?t=362&f=4) but I am still confused

Please post the workfile.

Done ;)

1. You can't estimate with cross-section weights since you have some cross-sections with only a single observation. In this case, the variance of the residuals within cross-section is 0.

2. You are going to have asymptotic bias issues if you estimate with cross-section fixed effects and a lagged endogenous variable with small T. Here's a quick summary

http://faculty.washington.edu/ezivot/ec ... slides.pdf

3. You'll have to restructure to cluster by industry.

4. Unless firms move between industries, you can't have both firm fixed effects and industry effects. They'll be colinear.

Thank you very much EViews Glenn for that clarification.So I think I will drop the fixed effects and rather include simple dummy variables for each industry. Still need to tackle autocorrelation though, I hope adding an AR(1) term and a robust covariance correction will do it.
Thanks!

Note that the AR term does implicitly add a lagged endogenous regressor.

I want to run a fixed effects model with HAC standard errors enabled....In cross section equation estimation window to enable Rob. SEs we go to options>and than choose EITHER "White" or "HAC Newey west(for time series data)"

How do I enable the same for panel data...as in equation estimation window, under options and panel options this feature is missing!

Please advice me in this regard.

The panel options tab has a Coef Covariance Method dropdown...