Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Joerg Luedicke <joerg.luedicke@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: nbreg - problem with constant? |

Date |
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. Joerg On Fri, Mar 2, 2012 at 11:11 AM, Richard Williams <richardwilliams.ndu@gmail.com> wrote: > 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: Richard.A.Williams.5@ND.Edu > WWW: http://www.nd.edu/~rwilliam > > > * > * 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/

**References**:**st: nbreg - problem with constant?***From:*Simon Falck <simon.falck@abe.kth.se>

**Re: st: nbreg - problem with constant?***From:*Richard Williams <richardwilliams.ndu@gmail.com>

- Prev by Date:
**Re: st: nbreg - problem with constant?** - Next by Date:
**st: SPWMATRIX error creating a social network matrix** - Previous by thread:
**Re: st: nbreg - problem with constant?** - Next by thread:
**Re: st: nbreg - problem with constant?** - Index(es):