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From |
"Bromiley, Philip" <bromiley@uci.edu> |

To |
"statalist@hsphsun2.harvard.edu" <'statalist@hsphsun2.harvard.edu'> |

Subject |
st: FW: xtabond2 chi squares |

Date |
Thu, 6 Sep 2007 10:04:54 -0700 |

I'm using xtabond2 with about 40,000 observations and 3500 panels. What I think are normal lags in the gmm give significant Hansen statistics. In covariance structure analysis (Lisrel etc.), with large samples you can almost always reject the hypothesis that the model fits the data. The large sample results in the test rejecting the hypothesis of equality between the estimated covariance matrix from the maximum likelihood and empirical matrix even if the two differ trivially (and they will always differ somewhat in practice). This has resulted in researchers in the area using a variety of fit indices instead of testing whether they can reject the hypothesis that the estimated and empirical matrices are equal. Is this problem possible with the Hansen and Sargan tests in xtabond2? As I understand Hansen (1982, lemma 4.1, pp. 1049), the test tests whether a particular matrix "converges in distribution to a normal random vector with mean zero and covariance matrix..." That is, would extremely large sample sizes cause the Hansen and Sargan tests to reject the null hypothesis of validity of instruments even if the differences are trivial? Phil Philip Bromiley Dean's Professor of Strategic Management Merage School of Business University of California, Irvine Irvine, CA 92697-3125 (949) 824-6657 (949) 725-2898 (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/

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