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Re: st: nbreg - problem with constant?
From
Richard Williams <[email protected]>
To
[email protected], "[email protected]" <[email protected]>
Subject
Re: st: nbreg - problem with constant?
Date
Fri, 02 Mar 2012 14:11:47 -0500
At 01:34 PM 3/2/2012, Simon Falck wrote:
Hi,
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 X5
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 = 576
LR
chi2(8) = 387.39
Dispersion = mean Prob > chi2 = 0.0000
Log likelihood = -562.09431 Pseudo R2 = 0.2563
Y Coef. Std.
Err. z P>z [95% Conf. Interval]
X1 .3927241 .3024751 1.30 0.194
-.2001162 .9855644
X2 .6401666 .4818861 1.33 0.184
-.3043129 1.584646
X3 1.27199 .4352673 2.92 0.003
.4188815 2.125098
X4 -5.603575 1.724484 -3.25 0.001
-8.983502 -2.223648
X5 -1.370085 .1557769 -8.80 0.000
-1.675402 -1.064768
Constant 10.5169 2.30579 4.56 0.000
5.997634 15.03617
/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: [email protected]
WWW: http://www.nd.edu/~rwilliam
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