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From |
"Austin Nichols" <[email protected]> |

To |
[email protected] |

Subject |
Re: st: determining standard errors of fixed effect terms after xtreg |

Date |
Fri, 1 Feb 2008 12:32:47 -0500 |

Sarah-- I haven't seen an answer on the list (let us know if you get any answers offlist), so I'll venture one. It may not be the one you were looking for. In short, if you want the standard errors of the fixed effects terms, put the fixed effects in as dummy variables. sysuse bpwide, clear qui xtreg bp_after bp_before sex, fe i(agegrp) robust predict fe_agegrp, u qui tab agegrp, gen(d) qui reg bp_after bp_before sex d*, nocons robust g fe=. g se=. forv i=1/3 { replace fe=_b[d`i'] if ageg==`i' replace se=_se[d`i'] if ageg==`i' } su fe, meanonly g fe_demeaned=fe-r(mean) tab fe_* If there are too many dummy variables in such a model to get estimates, the standard errors are probably meaningless anyway. Note that if you have panel data with N individuals in M groups over T time periods and you want to estimate M fixed effects at the group level, but you worry about serial correlation or other intra-group correlation and so cluster at the group level, the assumption is that M is close to infinity (or close enough) and there is no way to get good estimates of the sampling variation of M coefficients on dummy variables. Without clustering, if N=a*M, for some constant a, or nearly, e.g. roughly 25 kids per class, the asymptotic justification for standard errors means that as your data is getting infinitely large (or close enough) you are estimating infinitely many parameters (or close enough to infinity to be computationally infeasible) and the sampling variation of those parameter estimates is not going to be consistently estimated (since N increases at the same rate as M). However, if 25 is close enough to infinity for our purposes, we can get reasonable estimates, I suppose. I.e. we need the value of a to be "close" to infinity, regardless of N or M. That said, if you had no other X vars (only fixed effects), you could calculate the VCE by hand. This is easiest when you assume i.i.d. errors. The inverse of X'X when X is a bunch of dummies is just a MxM diagonal matrix with [1/m_i] on the diagonal where m_i is the number of cases in each group i. Since the VCE is the mean squared error times n/(n-k) times the inverse of X'X you can get the standard errors like so (the direct comparison is possible with 3 fixed effects, but not with many thousands, of course): sysuse bpwide, clear qui xtreg bp_after, fe i(agegrp) loc n=e(N) loc k=e(df_m)+1 predict double e if e(sample), e g double e2=e^2 su e2, meanonly loc e2=r(mean) levelsof agegrp, loc(levs) foreach v of local levs { qui count if e<. & agegrp==`v' mat v=nullmat(v),1/r(N) } mat v=diag(v') mat v=`e2'*`n'/(`n'-`k')*v mat li v qui tab agegrp, gen(d) qui reg bp_after d*, nocons mat li e(V) If the other X vars were uncorrelated with the fixed effects, you could extend this approach to include other X vars, I think, but that is unlikely to be the case in practice. A related note: You've seen Rothstein (2007), right? I mean http://www.princeton.edu/~jrothst/workingpapers/rothstein_VAM_20071120.pdf (referenced without a link in http://www-personal.umich.edu/~nicholsa/ciwod.pdf) which impugns these FE models for teacher effects on test score data. Rothstein finds that 5th grade teachers have similar size "effects" on 4th grade scores as 4th grade teachers have (i.e. the SD across teachers of estimated fixed effects is of comparable magnitude for both the teachers who can have a causal effect and those who can't), which means either (1) students are tracked based on scores to different kinds of teachers/classes, which means we can't interpret the "effects" as causal, or (2) the measured "effects" are just noise, for both 4th and 5th grade teachers. Or some convex combination of these explanations. I think. You might want to read the paper yourself. Am I interpreting your example right, that you are specifying a FE model with a lagged dependent variable on the right hand side? Do you want -xtabond- by chance? Then you'd have even more trouble recovering FE and SEs, I guess... On Jan 29, 2008 12:49 PM, Sarah Cohodes <[email protected]> wrote: > Dear Statalisters, > > I am trying to obtain the standard errors of the fixed effects terms > after -xtreg-. > > Using an analogous dataset to mine, obtaining the fe terms is easy: > > sysuse bpwide, clear > xtreg bp_after bp_before sex, fe i(agegrp) robust > predict fe_agegrp, u > > However, I don't know how to proceed to obtain the standard errors of > these fixed effects. > > I appreciate your time and effort in advance. I noticed that this > question has been asked but not answered on Statalist previously (see: > http://www.stata.com/statalist/archive/2007-01/msg01046.html or: > http://www.stata.com/statalist/archive/2006-04/msg00026.html). > I am loathe to revisit a topic that was not previously of interest or > perhaps has a trivial answer that I've overlooked, however I am quite > stuck. > > Again, thanks, > Sarah > > ******************************** > Sarah Cohodes > Data Manager and Research Analyst > Project for Policy Innovation in Education > Harvard Graduate School of Education > 617.496.3408 (phone) > 617.495.2614 (fax) * * 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/

**Follow-Ups**:**Re: st: determining standard errors of fixed effect terms after xtreg***From:*"Sarah Cohodes" <[email protected]>

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