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Re: st: nbreg - problem with constant?
Joerg Luedicke <email@example.com>
Re: st: nbreg - problem with constant?
Fri, 2 Mar 2012 11:35:03 -0800
In addition to what Richard said:
It may or may not be related to any potential non-intuitive results,
but I am wondering if you should include some kind of offset. Just
judging from your post I would think you need the number of firms in a
country from the previous year as an offset to basically model the
rate of new firms, instead of the absolute number of new firms.
Whether there are 10 firms in a country in year x and 15 firms in year
x+1 or 1000 firms in another country in year x and 1005 firms in year
x+1, is most likely a different story.
On Fri, Mar 2, 2012 at 11:11 AM, Richard Williams
> At 01:34 PM 3/2/2012, Simon Falck wrote:
>> I have some problems in fitting a negative binomial regression model. It
>> seems that one problem is related to the "constant" as the it inflates the
>> coef. If the constant is removed, some coef are still unexpectedly high.
>> Since removing the constant bias coef results implies restrictions, I hope
>> anyone can contribute with some insights on this matter.
>> I apply the NBREG command to estimate the nr of new firms per country
>> explained by country-characteristics. The dataset is consisted of
>> information for 72 countries over 8 years, N=id=576. The information is
>> annual, all regressors are lagged 1 year (t-1). The dv (Y) is the nr of new
>> firms per country and vary between 0-204. The indepv (X1-X5) are
>> country-specific attributes. Each indepv are continuous and vary across
>> countries (id). No interaction terms are used. Some correlation exist, in
>> general <0.3, but up to 0.6. The dataset is structured as,
>> id year Y X1 X2 X3 X4
>> 1 2000 10 0.5258504 1.148275 1.623761
>> 0.00905698 0.2926497
>> 2 2000 1 1.105136 0.9730458 0.7427208
>> 0.03010507 0.1732135
>> 3 2000 2 1.342283 0.7757816 0.6444564
>> 0.01280751 0.2596922
>> The model is estimated with command: nbreg Y X1 X2 X3 X4 X5
>> Generates results:
>> Negative binomial regression Number of obs =
>> LR chi2(8) =
>> Dispersion = mean Prob > chi2 =
>> Log likelihood = -562.09431 Pseudo R2 =
>> Y Coef. Std. Err. z P>z
>> [95% Conf. Interval]
>> X1 .3927241 .3024751 1.30 0.194 -.2001162
>> X2 .6401666 .4818861 1.33 0.184 -.3043129
>> X3 1.27199 .4352673 2.92 0.003 .4188815
>> X4 -5.603575 1.724484 -3.25 0.001 -8.983502
>> X5 -1.370085 .1557769 -8.80 0.000 -1.675402
>> Constant 10.5169 2.30579 4.56 0.000 5.997634
>> /lnalpha -.2836582 .1966372 -.66906 .1017437
>> alpha .753024 .1480725 .5121898 1.1071
>> Likelihood-ratio test of alpha=0: chibar2(01) = 214.48
>> Prob>=chibar2 = 0.000
>> The LR-test indicates that Negbin- is preferred over Possion. X1-X2 are
>> insignf., while X3-X5 are signf., P<0.05.
>> We can see that the constant is very large, coef= exp(10.5169)=33225.488
>> and std.err for X4 is quite high (1.72..).
> Without knowing more about the variables, I would be hesitant to say the
> constant is "large" or the standard error is "quite high." If you, say,
> rescaled the Xs, or centered each X about its mean, the constant would
> change. Likewise if you rescaled X4 (e.g. changed it from income in dollars
> to income in thousands of dollars) the coefficient and standard error for X4
> would change. You can think of the constant as being the score a case would
> have if every X equaled 0, but there may be no such cases where that would
> ever happen, e.g. in a sample of adults nobody will have a value of 0 years.
> In short, it isn't clear to me that you have a problem. If you find the
> coefficients non-intuitive, then rescaling the Xs in some way or centering
> them may help.
> As a sidelight, your analysis seems to be ignoring your panel structure. You
> may wish to take a look at the XT manual and/or Paul Allison's book on
> "Fixed effects regression models."
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME: (574)289-5227
> EMAIL: Richard.A.Williams.5@ND.Edu
> WWW: http://www.nd.edu/~rwilliam
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