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From |
vwiggins@stata.com (Vince Wiggins, StataCorp) |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Hausman test implementation - what's on Base Ref. Manual V4, |

Date |
Wed, 18 Feb 2004 14:45:59 -0600 |

Michael Creel <michael.creel@uab.es> asks how the covariance matrix is formed in the -hausman- command, > I'm not a Stata user, but a reviewer of a research paper of mine has > referred me to the Stata Base Reference Manual, Vol. 4, pages > 126-147. Not being a user, I don't have a copy of that around, and > our library doesn't either. I'm interested to know how the Hausman > test is implemented in Stata, Version 8. Specifically, how is the > variance of the difference of parameters calculated? Is it simply > the difference of the two separate estimated covariances, or > something more complicated? The -hausman- command implements the test as proposed by Hausman (1978, Econometrica), meaning that the variance-covariance matrix (VCE) for the test is just the difference of the VCEs of the consistent estimator and the fully efficient estimator. For a test of two specific estimators, there are generally a number of augmented regression tests that are asymptotically equivalent to the Hausman test; see for example Davidson and MacKinnon, Econometric Theory and Methods, 2004 for examples using instrumental variables regression; or Baltagi, 2001, Econometric Analysis of Panel Data, for examples using panel data. These augmented regression tests often have better behavior in finite samples, in that they never produce tests that cannot be computed due to non-positive-definite matrices, but not necessarily better properties. If both of the estimators produce observation-level scores, the -suest- command can be used to perform a test similar to the the Hausman test, but one that does not require either estimator to be fully efficient. In particular, -suest- (seemingly unrelated estimation) uses the scores to form a full VCE of both sets of estimates. Because this VCE is formed using the sandwich estimator (linearization/Huber/white estimator), it is robust to many distributional assumptions and is amenable to clustering. To find out more, see [R] suest or Weesie, 1999, Stata Technical Bulletin, STB-52. -- Vince vwiggins@stata.com * * 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**:**st: hausman test - found where to get the technical bulletin***From:*Michael Creel <michael.creel@uab.es>

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