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From |
"Ryan D. Edwards" <redwards@hsph.harvard.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: ivreg2 and estimation under autocorrelation |

Date |
Mon, 21 Feb 2005 18:08:29 -0500 (EST) |

The last time I wanted to estimate a time-series model in the presence of autocorrelation and endogenous RHS variables, I used TSP, which had a nifty AR() command for essentially doing Stata's "prais" or old "corc" command with instrumental variables. I see that Baum, Schaffer, and Stillman have expertly provided the Stata community with a new version of ivreg2, which I think can correct the var-covar matrix for heteroscedasticity and for autocorrelation. My concern is that ivreg2 may be getting the standard errors right but not necessarily the coefficient estimates, at least not in the way traditional corrections for autocorrelation typically do. I'm concerned because in the phillips.dta example discussed in the helpfile, the parameter estimates do not change when the bw() option is used. That is, . ivreg2 cinf unem . ivreg2 cinf unem, bw(3) . ivreg cinf unem . reg cinf unem all yield exactly the same coefficient estimates. (The standard errors are a little different across most but not all of these.) By contrast, if we run . prais cinf unem we obtain different estimates and standard errors. I was under the impression that this outcome is to be expected after correcting for autocorrelation. While OLS is technically unbiased, because the direction of bias is unknown and therefore should be zero on average, in practice autocorrelation acts as an omitted variable, moving the estimates in one direction or another, even though it is unknown ex ante. So it would be odd to see the estimates remain exactly the same after autocorrelation were corrected for. Am I wrong to be concerned? Or am I perhaps missing the spirit of the autocorrelation correction in ivreg2? Is there a different Stata algorithm that does what I'm looking for? Ryan D. Edwards, Ph.D. redwards@hsph.harvard.edu Postdoctoral Fellow in the Study of Aging, RAND Corporation and Visiting Scientist, 2004-2005 Department of Population and International Health Harvard School of Public Health * * 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: ivreg2 and estimation under autocorrelation***From:*Mark Schaffer <M.E.Schaffer@hw.ac.uk>

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