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From |
"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: loglikelihood and loglikelihood ratio |

Date |
Tue, 17 Mar 2009 13:57:50 -0700 |

I don't pretend to understand all you have done. However, should the degrees of freedom be approaching or exceeding the number of observations? In your first model you have 72 observations and estimate 40+24=64 parameters (getting close to saturation). In your second model you have 72 observations and 58 parameters estimated. Third model 54 observations and 53 parameters Fourth model 54 observations and 47 parameters estimated. Are you saturating the model? Are there some linear dependencies that are causing the ills? Tony Peter A. Lachenbruch Department of Public Health Oregon State University Corvallis, OR 97330 Phone: 541-737-3832 FAX: 541-737-4001 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of jjc.li@utoronto.ca Sent: Tuesday, March 17, 2009 11:11 AM To: statalist@hsphsun2.harvard.edu Subject: RE: st: loglikelihood and loglikelihood ratio 1. First, My euqation system is one translog cost function (lnc), with 4 cost share equatios(sl sk sm se). To use -sureg-, I just need to estimate the translog cost function with 3 cost share equations (choose any 3 of the 4) My total dataset contains 7 regions. The estimation results for the 7 regions are fine. All R square are positive, all LR chi() are positive, and all degree of freedom are right. 2. I choose the last 4 regions of the total 7regions and create a new data set(changed the dummy variables). Here, all the R square are positive, all LR chi() are positive. But the degree of freedom are strange. 4 regions Unrestricted ---------------------------------------------------------------------- Equation Obs Parms RMSE "R-sq" chi2 P ---------------------------------------------------------------------- lnc 72 40 .0987079 0.9693 7.62e+07 0.0000 sl 72 8 .0230819 0.3033 417.70 0.0000 se 72 8 .0023162 0.9399 1246.72 0.0000 sm 72 8 .0292372 0.5744 1094.54 0.0000 ---------------------------------------------------------------------- _cons in equation lnc are dropped, no other variable droped 4 regions Restricted ---------------------------------------------------------------------- Equation Obs Parms RMSE "R-sq" chi2 P ---------------------------------------------------------------------- lnc 72 37 .0930231 0.9727 1.47e+07 0.0000 sl 72 7 .0195899 0.4982 347.11 0.0000 se 72 7 .0022661 0.9425 1275.65 0.0000 sm 72 7 .0270912 0.6346 1003.91 0.0000 ---------------------------------------------------------------------- No _cons dropped, no variable dropped Likelihood-ratio test LR chi2(2) = 6.71 (Assumption: B nested in A) Prob > chi2 = 0.0350 I am thinking if the degree of freedom changed from 3 to 2 because of _cons in unrestricted model is dropped, but kept in restricted model? 3. I chose the first 3 regions and created them as a new dataset (changed the dummy variables). When I estimated equations lnc, sl, sm, se, there are two negative R square values. So I changed them to ln, sl, sk, se and got one negative R-sq this time. LR chi() here are negative. 3 regions unrestriced ---------------------------------------------------------------------- Equation Obs Parms RMSE "R-sq" chi2 P ---------------------------------------------------------------------- lnc 54 32 .0711293 0.9520 1.53e+08 0.0000 sl 54 7 .0517401 -0.9964 1022.17 0.0000 sk 54 7 .0089583 0.5192 1238.82 0.0000 sm 54 7 .0345315 0.5581 701.24 0.0000 ---------------------------------------------------------------------- lnq, _cons are dropped in equation lnc 3 regions restricted ---------------------------------------------------------------------- Equation Obs Parms RMSE "R-sq" chi2 P ---------------------------------------------------------------------- lnc 54 29 .061907 0.9637 1.27e+08 0.0000 sl 54 6 .0239187 0.5733 366.81 0.0000 sk 54 6 .0085489 0.5621 431.86 0.0000 sm 54 6 .0259183 0.7511 405.20 0.0000 ---------------------------------------------------------------------- _cons in equation lnc is dropped, no other variables dropped. Likelihood-ratio test LR chi2(3) = -5.07 (Assumption: E nested in A) Prob > chi2 = 1.0000 Jingjing Quoting "Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>: > I haven't been following this in detail, but one issue that might > simplify matters would be for Jingjing to copy the commands from the > results window and the error messages received. Only copy the relevant > parts of the output - I don't want to see 15 pages of garbage. Maarten > has been very noble in this. > > Tony > > Peter A. Lachenbruch > Department of Public Health > Oregon State University > Corvallis, OR 97330 > Phone: 541-737-3832 > FAX: 541-737-4001 > > > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of > jjc.li@utoronto.ca > Sent: Tuesday, March 17, 2009 9:45 AM > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: loglikelihood and loglikelihood ratio > > 1. I just checked the commands for the 3 regions case and found they > are right. But in the estimation of the unrestricted model, R square > of one of the three equations are negative, R square of the other > three euqations are positive. Does it cause the negative LR chi2 value? > > 2. In my 7 regions case, LR chi are positive. However, there's some > strange thing about the degree of freedom. In unrestricted case, the > parameters for equation 1, 2, 3 are 40, 8, 8, respectively. In > restricted case, are 37, 7, 7. I though the degree of freedom should > be 40-37=3. But the result of lrtest given by stata is LR chi(2). > What's the problem? > > Thanks. > > Jingjing > > > > Quoting Maarten buis <maartenbuis@yahoo.co.uk>: > >> >> You definately should not use the -force- option. I was expecting >> that you were not telling us everything you did. >> >> -- Maarten >> >> ----------------------------------------- >> Maarten L. Buis >> Institut fuer Soziologie >> Universitaet Tuebingen >> Wilhelmstrasse 36 >> 72074 Tuebingen >> Germany >> >> http://home.fsw.vu.nl/m.buis/ >> ----------------------------------------- >> >> >> --- On Tue, 17/3/09, jjc.li@utoronto.ca <jjc.li@utoronto.ca> wrote: >> >>> From: jjc.li@utoronto.ca <jjc.li@utoronto.ca> >>> Subject: Re: st: loglikelihood and loglikelihood ratio >>> To: statalist@hsphsun2.harvard.edu >>> Date: Tuesday, 17 March, 2009, 2:59 PM >>> I am quite sure it's the same 3 regions. Because I just >>> input the 3 >>> regions dataest. I will try to use-force-, then run it >>> again. >>> >>> Thank you. >>> >>> Quoting Maarten buis <maartenbuis@yahoo.co.uk>: >>> >>> > >>> > --- On Tue, 17/3/09, jjc.li@utoronto.ca wrote: >>> >> The previous results is from the estimation of a >>> "7 >>> >> regions dataset". >>> >> >>> >> Then I use almost the same command to do the >>> estimation of >>> >> a "3 regions dataset". The only thing I >>> change is that I >>> >> choose first 3 regions of the total 7 regions and >>> also >>> >> modify the command that related to the dummy >>> varible. This >>> >> time, it gives a negative value. >>> > >>> > Are you sure both models A and E refer to the same 3 >>> regions? >>> > >>> > Did you specify the -force- option in -lrtest-? >>> > >>> > --Maarten >>> > >>> > ----------------------------------------- >>> > Maarten L. Buis >>> > Institut fuer Soziologie >>> > Universitaet Tuebingen >>> > Wilhelmstrasse 36 >>> > 72074 Tuebingen >>> > Germany >>> > >>> > http://home.fsw.vu.nl/m.buis/ >>> > ----------------------------------------- >>> > >>> > >>> > >>> > >>> > >>> > >>> > >>> > * >>> > * 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/ >> >> >> >> >> * >> * 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/ > > * > * 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/ * * 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**:**RE: st: loglikelihood and loglikelihood ratio***From:*jjc.li@utoronto.ca

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