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From |
Talal <talalesm@yahoo.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: RE: Different results for Fixed and Random Effects models Using STATA and SAS |

Date |
Tue, 11 May 2010 11:41:26 -0700 (PDT) |

Dear Nick, Thanks again for your comment. I need to validate my STATA result before I can proceed with my analysis (PhD Research), SAS gives just slightly different results for Random Effect but major one for Random Effect. and it Hausman Test results is really Odd. I will be very thankful if you can lead me to a person or contact who can help me. Regards Talal --- On Tue, 11/5/10, Nick Cox <n.j.cox@durham.ac.uk> wrote: > From: Nick Cox <n.j.cox@durham.ac.uk> > Subject: RE: st: RE: Different results for Fixed and Random Effects models Using STATA and SAS > To: statalist@hsphsun2.harvard.edu > Date: Tuesday, 11 May, 2010, 10:16 > Thanks for this. Those with expert > knowledge of knowledge of fixed and random effects models > (not me) should be in a better position to comment or to ask > for specific extras. > > You should probably be using an extra dummy for North-East > England.... > > Nick > n.j.cox@durham.ac.uk > > > Talal > > Dear Nick, > > Thanks for your comment and sorry for my shallow > explanations of the problem. Below is the command and result > comparison between SAS and STATA. > I have slightly unbalaced data for 4 regional areas of GB. > > > a- For Fixed Effect: > > SAS: > > proc reg data=chapter3.all_area; > Model Ln_qdt = Ln_qdt2 Ln_vkm Ln_income Ln_F > deregulation_dummy Time_Trend London_dummy Mets_ dummy > Scotland_ dummy Wales_ dummy; > test London_dv = Mets_dv = Scotland_dv = Wales_dv = 0 > ; > run; > > > STATA: > > regress Ln_qdt Ln_qdt2 Ln_vkm Ln_income > Ln_F deregulation_dummy Time_Trend London_dummy Mets_ dummy > Scotland_ dummy Wales_ dummy > > > > > b- For Random Effect: > > SAS: > > proc panel data=chapter3.all_area; > ID area year; > Model Ln_qdt = Ln_qdt2 Ln_vkm Ln_income Ln_F > deregulation_dummy Time_Trend / RANONE BP VCOMP=WK > ; > run; > > > > STATA: > > iis area > xtreg qdt Ln_qdt2 Ln_vkm Ln_income Ln_F deregulation_dummy > Time_Trend, re theta > > SAS > > Model FE > RE FE (STATA) > RE(STATA) > Coeff. > Coeff. > Coeff. Coeff. > > > Ln F -.108 > -0.09892 > -.108 -0.06321 > Ln VKM .114 > 0.135992 > .115 0.082707 > Ln Income -.560 > -0.52503 > -.566 -0.297 > Ln Qdt-1 .695 > 0.747298 > .692 0.924061 > Der. DV -.046 > -0.04975 > -.047 -0.05055 > TT .011 > 0.010877 > .011 0.00887 > > > Mets .196 > > 0.198 > Scot .153 > > 0.154 > Wales -.023 > > -0.023 > constant > 5.999 > 5.908 > > > F > 3624.282 > 3618.020 > R2 (Adj.) .997 > 0.973 > 0.9969 0.9967 > Durbin-Watson > 1.703 > > > (Incremental) F 5.57 > (0.0015) 5.57 (0.0015) > Breusch Pagan Test 0.00 > (0.9781) 0.00 (0.9779) > Hausman Test 2.34 > (0.8859) 20.16 (0.0026) > > > > --- On Mon, 10/5/10, Nick Cox <n.j.cox@durham.ac.uk> > wrote: > > > From: Nick Cox <n.j.cox@durham.ac.uk> > > Subject: st: RE: Different results for Fixed and > Random Effects models Using STATA and SAS > > To: statalist@hsphsun2.harvard.edu > > Date: Monday, 10 May, 2010, 10:34 > > This is only a small distance away > > from "I got different results from different programs > and > > don't understand why". People who know about these > commands > > need to see exactly what you typed in both programs so > that > > they can be sure that the commands are exactly > equivalent. > > They also would find it difficult to comment unless > your > > results are phrased in terms of datasets that everyone > can > > access. > > > > Nick > > n.j.cox@durham.ac.uk > > > > > > Talal > > > > I have estimated Fixed and Random Effects models for > panal > > data which are slightly unbalanced using SAS and > STATA > > > > the two softwa estimated totally different parametres > for > > Ranndom Effects Model, and slightly different one for > Fixed > > Effects one res. > > > > Is this due to diffrent estimation approches? What > are > > these since I have to report them on my study? > > > > For Hausman test: I also got major differences between > the > > two softwares. > > > > Is this due to diffrent estimation approches too? What > are > > these? > > > > Is the 2 software deals with unbalanced data in > different > > ways? > > > > I am very thankful for any answers. > > > > * > > * 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: RE: Different results for Fixed and Random Effects models Using STATA and SAS***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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