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sigmamore specifies that the covariance matrices be based on the estimated disturbance variance
from the efficient estimator. This option provides a proper estimate of the contrast variance for
so-called tests of exogeneity and overidentification in instrumental-variables regression.
This is the furthest I can go. Hopefully you'll get further explanation.
sigmamore specifies that the covariance matrices be based on the estimated disturbance variance
from the efficient estimator. This option provides a proper estimate of the contrast variance for
so-called tests of exogeneity and overidentification in instrumental-variables regression.
This is the furthest I can go. Hopefully you'll get further explanation.
Maham:
just an aside to what Marcos has already comprehensively explained, obtained from -hausman- entry in Stata 13.1 pdf manual (pag 768):
-sigmamore- option for -hausman- specification test allows the covariance matrices of both estimators to be based on the estimated disturbance variance of the efficient estimator;
-sigmaless- option for the same test allows the covariance matrices of both estimators to be based on the estimated disturbance variance of the consisten estimator;
- sigmamore - option for -hausman- has a more econometric flavour than -sigmaless- , in that, quoting Stata 13.1 -pdf manual related entry (same page),
...provides a proper estimate of the contrast variance for so-called tests of exogeneity and overidentification in instrumental-variables regression
Maham:
just an aside to what Marcos has already comprehensively explained, obtained from -hausman- entry in Stata 13.1 pdf manual (pag 768):
-sigmamore- option for -hausman- specification test allows the covariance matrices of both estimators to be based on the estimated disturbance variance of the efficient estimator;
-sigmaless- option for the same test allows the covariance matrices of both estimators to be based on the estimated disturbance variance of the consisten estimator;
- sigmamore - option for -hausman- has a more econometric flavour than -sigmaless- , in that, quoting Stata 13.1 -pdf manual related entry (same page),
Hi all!! I am sonia kaur. I am currently working on my thesis ( determinants of bank profitability) in which i have to analyse approximately 17000 banks over 64 quarters. The determinants i have chosen are lag roa, size , credit risk ratio, a few more ratios as well and inflation , gdp and interest rates.
I have a few issues that i need to clarify and i really hope i can get some insights from you guys.
1) firstly i did pooled regression and used the xtset command, my data is identified as unbalanced panel snd over 64 quarters. ( this includes the lagroa variable).
Then i go on to run my fe and re using the same variables as the pooled but the problem i face is in the hausman test. My test is giving me a positive definite error and i did some research and tried to change the commands to fe_all, re_all , store both of them and then use the fommand " hausman fe_all re_all, sigmamore. And i do not get the error anymore. Now my question is can i actually do this to solve the problem or am i forcing the data to work my way by using this commands? Is there an underlying problem i am not seeing? Im really confused. Please do give me your insights.
Sonia:
welcome to this forum.
Although exceptions do exist, usually -xtreg- outperforms pooled OLS when it comes to panel data.
It is pretty frequent that -hausman- gives back the message you mention.
The -sigmamore- option often fixes the issue.
Eventually, your code for -hausman- is correct.
As an aside, please note that Stata can handle both balanced and unbalanced panel datasets, the latter with no extra effort/details from your side.
Carlo Lazzaro thats great news!! Thankyou so so much! Can i just check with you , if my intial data is auto set as quarter for this regression is there any issues i might face as i am more familiar with running regressions using yearly data. So i was wondering if the error i was receiving could be because of that.
Sonia:
I do not think that quarters are an issue.
-hausman- works asyntotically: hence, stumbling upon some methodological cabblestone along the way is the rule rather than the exception.
On a different note, I would be more concerned about: endogeneity; model misspecification (eg, non-linearity) and (less relevant but sometimes annoying) heteroskedasticity.
Carlo Lazzaro okay carlo!! Thankyou so much! Are there any tests you suggest i run to check for those problems mentioned above?? Cause ive done a fe re and hausman test now ( thankgod it was correct) . My next test is gmm. Do you think gmm will solve those issues ( if present) ?
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