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From |
Simon Falck <[email protected]> |

To |
"[email protected]" <[email protected]> |

Subject |
st: nbreg - problem with constant? |

Date |
Fri, 2 Mar 2012 18:34:13 +0000 |

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..). If the constant is removed: nbreg Y X1 X2 X3 X4 X5, noconstant ----------------- Negative binomial regression Number of obs = 576 Dispersion = mean Wald chi2(8) = 457.53 Log likelihood = -600.50218 Prob > chi2 = 0.0000 Robust Y Coef. Std. Err. z P>z [95% Conf. Interval] X1 .3932589 .326686 1.20 0.229 -.247034 1.033552 X2 2.416205 .4947226 4.88 0.000 1.446567 3.385844 X3 1.410318 .4546365 3.10 0.002 .5192471 2.301389 X4 3.029976 1.402828 2.16 0.031 .2804839 5.779468 X5 -.6341323 .1417935 -4.47 0.000 -.9120425 -.3562222 /lnalpha .251133 .1629578 -.0682585 .5705246 alpha 1.285481 .2094792 .934019 1.769195 Likelihood-ratio test of alpha=0: chibar2(01) = 276.18 Prob>=chibar2 = 0.000 ----------------- The results (coef) significantly changes. EFor example, X3 has coef exp(1.27199)= 3.5679457 for the first model and exp(2.416205)= 11.203262 for the second model. X4 changes sign from (-) to (+). In general, the coef are unexpectedly/unreasonable large. Are these "large" coef due to collinear variables (although VIF~2 and Tolerance is >0.5) or misspecification of the model? Can you think of something that I do wrong? Thanks in advance, /Simon * * 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/

**Follow-Ups**:**Re: st: nbreg - problem with constant?***From:*David Hoaglin <[email protected]>

**Re: st: nbreg - problem with constant?***From:*Richard Williams <[email protected]>

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