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Re: st: RE: intreg with fixed effects and clustered standard errors
From
Nick Cox <[email protected]>
To
[email protected]
Subject
Re: st: RE: intreg with fixed effects and clustered standard errors
Date
Thu, 1 Mar 2012 00:20:09 +0000
On this evidence, your model is too complex and/or inappropriate to
fit to your data. How many parameters are you trying to estimate? One
strategy is to retreat to much simpler models and get something
working before trying to complicate it.
Nick
On Wed, Feb 29, 2012 at 7:21 PM, Pamela Campa <[email protected]> wrote:
> Thanks for your reply
>
> If I use intreg it does not estimate some standard errors. The data are on
> log emissions; for most of the plants I have point data, but for plants that
> release substances below a certain level I have interval-coded data. This is
> how the output looks like:
>
> . xi: intreg llnemissions ulnemissions L1lndistance_year lidensity_bu5
> industry_id1-industry_id20 industry_id22-industry_id64 i.countyid if
> year>=2001, vce(cluster firm_code)
> i.countyid _Icountyid_1-1568 (_Icountyid_1 for coun~id==01001
> omitted)
> note: industry_id35 omitted because of collinearity
> note: industry_id36 omitted because of collinearity
> note: industry_id38 omitted because of collinearity
> note: industry_id39 omitted because of collinearity
> note: industry_id41 omitted because of collinearity
> note: industry_id42 omitted because of collinearity
> note: industry_id46 omitted because of collinearity
> note: industry_id49 omitted because of collinearity
> note: industry_id54 omitted because of collinearity
> note: industry_id55 omitted because of collinearity
> note: industry_id56 omitted because of collinearity
> note: industry_id61 omitted because of collinearity
> note: _Icountyid_1232 omitted because of collinearity
> note: _Icountyid_1373 omitted because of collinearity
>
> Fitting constant-only model:
>
> Iteration 0: log pseudolikelihood = -421061.15
> Iteration 1: log pseudolikelihood = -420990.91
> Iteration 2: log pseudolikelihood = -420990.91
>
> Fitting full model:
>
> Iteration 0: log pseudolikelihood = -407783.52
> Iteration 1: log pseudolikelihood = -407606.27
> Iteration 2: log pseudolikelihood = -407606.26
>
> Interval regression Number of obs = 134769
> Wald chi2(1574) = 0.00
> Log pseudolikelihood = -407606.26 Prob > chi2 = 1.0000
>
> (Std. Err. adjusted for 24839 clusters in
> firm_code)
> ------------------------------------------------------------------------------
> | Robust
> | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------+----------------------------------------------------------------
> L1lndistan~r | -.0103242 . . . . .
> lidensity_~5 | -.4127465 . . . . .
> and the standard errors are also not estimated for some industry and county
> fixed effects
>
> Thanks
> Pamela
>
>
>
>
>
>
> On 2/29/2012 6:36 PM, Nick Cox wrote:
>>
>> -intreg2- qualifies as user-written; you are asked to specify this.
>>
>> However, in this particular case hasn't the functionality been folded into
>> -intreg-?
>>
>> That aside, you are asking for guidance on a key issue without telling us
>> anything precise about the data or showing any output.
>>
>> Nick
>> [email protected]
>>
>> Pamela Campa
>>
>> I'm trying to estimate a regression model with an interval-coded
>> dependent variable. I have plant-level data for several years, and I
>> regress an interval coded dependent variable on some continuous X's,
>> industry and county fixed effects, and state by year shocks.
>>
>> I cluster the standard errors by plant. I use the command intreg2. The
>> Stata output gives standard errors for all the variables I put in, but
>> it does not show the Wald chi(2) statistic. I did try to remove plants
>> that appear only once in the dataset, counties for which there is only
>> one plant and industries for which there is only one plant, but that
>> does not fix the problem.
>>
>> Could anyone please suggest a way to deal with this?I'm not necessarily
>> interested in the Wald chi(2) , but I'm afraid that the fact that it is
>> missing signals some misspecification in my model.
>>
>> Moreover, my pseudo likelihood is as low as -590000. Is that worrisome?
>>
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
>
>
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/