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From |
Joseph Coveney <[email protected]> |

To |
[email protected] |

Subject |
Re: st: AREG vs. XTREG, FE |

Date |
Wed, 20 Nov 2002 03:06:07 +0900 |

Sandra Kalev asked why she was getting different standard errors for parameter estimates with -xtreg, fe- and -areg, absorb()-. She also asked about differences in methods between -xtreg, fe-, -areg, absorb()- and -regress- with dummy (indicator) variables for units. ----------begin excerpt from Sandra's post------------- I am analyzing pooled data of organizations' workforce composition, using STATA 7. I need to run robust fixed effects regression. I am thinking of using AREG for that. I have two - related - questions: 1. Why AREG and XTREG (FE) don't produce the same results? The standard errors produced by AREG are smaller than those produced by XTREG. 2. What is the difference between running fixed effects using dummies for each unit (e.g. xi: regress depvar indepvars i.org_name) and running fixed effects using differences from the mean (as i think XTREG, FE does)?. Is there a substantive difference or is it just about playing around with the algebra? What does AREG use? ------------end excerpt-------------- I don't know why Sandra gets different standard errors, but it might be due to subtle inconsistencies in the way she has set up the model in each case. The two methods produce identical results in fictional datasets without a within-unit correlation and with ca. 70% correlation within unit. (See the do-file below.) As to the second question, I believe that Stata Corp. has a FAQ on this. There is no difference between using dummy (indicator) variables for the units and performing OLS regression with either -areg, absorb()- or -xtreg, fe-. I don't think that -xtreg, fe- uses any different algorithm from the other two commands. I believe that all three commands use essentially the same method, which is the same method as randomized-blocks ANOVA (-anova depvar indvar blocks-). Joseph Coveney ------------begin do file------------- set more off * * uncorrelated case * clear set obs 40 set seed 20021119 generate byte org_name=_n forvalues tim=1/3 { generate int dep`tim'= /* */round(100.0+15.0*invnorm(uniform()), 1) } reshape long dep, i(org_name) j(tim) xi: xtreg dep i.tim, i(org_name) fe xi: areg dep i.tim, absorb(org_name) quietly tabulate org_name, generate(dum) drop dum40 xi: regress dep i.tim dum* test _Itim_2 _Itim_3 anova dep tim org_name xi: areg dep i.tim, absorb(org_name) robust xi: regress dep i.tim dum*, robust * * correlated case * drawnorm dep1 dep2 dep3, n(40) /* */ means(100 100 100) sd(15 15 15) /* */ corr(1.0, 0.7 0.7 \ 0.7, 1.0, 0.7 \ 0.7, 0.7, 1.0) /* */ clear generate byte org_name=_n reshape long dep, i(org_name) j(tim) xi: xtreg dep i.tim, i(org_name) fe xi: areg dep i.tim, absorb(org_name) quietly tabulate org_name, generate(dum) drop dum40 xi: regress dep i.tim dum* test _Itim_2 _Itim_3 anova dep tim org_name xi: areg dep i.tim, absorb(org_name) robust xi: regress dep i.tim dum*, robust exit -------------end do file-------------- * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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