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From |
"Joao Ricardo F. Lima" <[email protected]> |

To |
[email protected] |

Subject |
Re: st: glm and reg produce different results for loglinear model? |

Date |
Fri, 31 Oct 2008 20:44:25 -0300 |

Dear Matthew, see this Maarten's answer: http://www.stata.com/statalist/archive/2008-10/msg01362.html it´s other case, but I think that you can understand the difference. HTH, Joao Lima 2008/10/31 Matthew Mercurio (matthewmercurio) <[email protected]>: > I have two variables, > > (1) outagecost (estimated costs to each customer of a short electrical > power interuuption) > (2) mwhannual (annual megawatt hours of electricity consumption fpr each > customer) > > Since these variables appear approximately lognormal, I have been > estimating the following simple model: > > reg lnoutagecost lnmwhannual > > where lnoutagecost and lnmwhannual represent the natural log of the two > variables desribed above. The results are: > > . reg lnoutagecost lnmwhannual > > Source | SS df MS Number of obs = > 32345 > -------------+------------------------------ F( 1, 32343) = > 9370.20 > Model | 34151.9301 1 34151.9301 Prob > F = > 0.0000 > Residual | 117881.722 32343 3.6447368 R-squared = > 0.2246 > -------------+------------------------------ Adj R-squared = > 0.2246 > Total | 152033.652 32344 4.70052104 Root MSE = > 1.9091 > ------------------------------------------------------------------------ > ---- > lnoutagecost | Coef. Std. Err. t P>|t| [95% Conf. > Interval] > -------------+---------------------------------------------------------- > ---- > lnmwhannual | .3824726 .0039512 96.80 0.000 .3747282 > .3902171 > _cons | 5.370938 .0232302 231.21 0.000 5.325406 > 5.41647 > ------------------------------------------------------------------------ > ---- > > I then tried the following model in glm which I had expected to produce > identical results: > > glm outagecost lnmwhannual, link(log) > > Generalized linear models No. of obs = > 52418 > Optimization : ML Residual df = > 52416 > Scale parameter = > 7.59e+09 > Deviance = 3.97873e+14 (1/df) Deviance = > 7.59e+09 > Pearson = 3.97873e+14 (1/df) Pearson = > 7.59e+09 > Variance function: V(u) = 1 [Gaussian] > Link function : g(u) = ln(u) [Log] > AIC = > 25.5881 > Log likelihood = -670636.5416 BIC = > 3.98e+14 > ------------------------------------------------------------------------ > ---- > | OIM > outagecost | Coef. Std. Err. z P>|z| [95% Conf. > Interval] > -------------+---------------------------------------------------------- > ---- > lnmwhannual | .5568004 .0130092 42.80 0.000 .5313029 > .5822979 > _cons | 5.355758 .1384432 38.69 0.000 5.084414 > 5.627102 > ------------------------------------------------------------------------ > ---- > > Obviously the results are very similar, but not identical. > > I read the Stata Manual section on GLM and checked a large number of > posts on Statalist related to loglinear models, but I was not able to > understand exactly why glm using link(log) doesn't produce the same > results as logging both variables and using reg. Based on my reading > of the Stata manual it appears to have someing to do with the fact that > the link() option relates to the expectation od the dependent variable, > not the dependent variable itself. Can anyone tell me why the results > are different? > > Matthew G. Mercurio, Ph.D. > Senior Consultant > Freeman, Sullivan & Co. > > > > * > * 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/ > -- ------------------------------- Joao Ricardo Lima Professor UFPB-CCA-DCFS +553138923914 ------------------------------- * * 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: glm and reg produce different results for loglinear model?***From:*"Matthew Mercurio (matthewmercurio)" <[email protected]>

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