[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
John Antonakis <john.antonakis@unil.ch> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: re: ivreg2: No validity tests if just-identified? |

Date |
Sat, 18 Apr 2009 21:28:56 +0200 |

Hi Kit: Thanks for clarifying further; I agree with your reasoning. As for your question, yes, the estimates change substantially for the second-stage equation (the first stage are, of course, unchanged): Two-Tailed Estimate S.E. Est./S.E. P-Value LW ON IQ 0.004 0.001 3.805 0.000 S 0.094 0.007 13.667 0.000 EXPR 0.046 0.006 7.231 0.000 Cons 3.911 0.110 35.652 0.000 This is what they were before (with the error estimated): LW ON IQ 0.022 0.012 1.815 0.070 S 0.040 0.038 1.050 0.294 EXPR 0.051 0.008 6.280 0.000 Cons 2.789 0.771 3.618 0.000 This is what ivreg2 with liml gives: lw Coef. Std. Err. z P>z [95% Conf. Interval] iq 0.02194 0.012088 1.81 0.07 -0.00175 0.045632 s 0.039556 0.037688 1.05 0.294 -0.03431 0.113424 expr 0.050968 0.008116 6.28 0 0.03506 0.066875 _cons 2.789476 0.771075 3.62 0 1.278197 4.300754 Best, John. ____________________________________________________ Prof. John Antonakis Associate Dean Faculty of Business and Economics University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 ____________________________________________________ On 18.04.2009 15:06, Kit Baum wrote:

<> I can reproduce the results below withuse http://fmwww.bc.edu/ec-p/data/hayashi/griliches76.dta ivreg2 lw s expr (iq=med), endog(iq) liml firstusing LIML rather than FIML. Indeed, your intuition that restrictingthe two equations' errors to be uncorrelated gives rise to theWu-Hausman endogeneity statistic is correct. However in terms ofsemantics I would not describe the restriction of the equations' errorcovariance as an 'identifying restriction'. It is certainly true thatrestrictions on error covariances may be used to identify equations,but in this case the equation is already identified by the order andrank conditions. If you impose that restriction a priori, it seems tome that you're using a different estimation procedure. Do the resultsfrom the constrained estimation differ from OLS results for the LWequation?Kit Baum | Boston College Economics & DIW Berlin |http://ideas.repec.org/e/pba1.htmlAn Introduction to Stata Programming| http://www.stata-press.com/books/isp.htmlAn Introduction to Modern Econometrics Using Stata |http://www.stata-press.com/books/imeus.htmlOn Apr 18, 2009, at 02:33 , John wrote:Thanks for the note. I like the endog option and I see what your endog test is doing--it seems to me that it is constraining the residual covariance to zero (this is what I meant by overidentifying test--which I see is not one in the classical sense). As for constraining the residuals I can accomplish this (and obtain a similar result to what your endog test does) using Mplus to estimate the system of equations you note below. The estimator is maximum likelihood estimation. Estimating the covariance I obtain: Estimate S.E. Est./S.E. P-Value IQ ON S 2.876 0.205 14.022 0.000 EXPR -0.239 0.207 -1.153 0.249 MED 0.482 0.164 2.935 0.003 Cons 60.467 2.913 20.759 0.000 LW ON IQ 0.022 0.012 1.815 0.070 S 0.040 0.038 1.050 0.294 EXPR 0.051 0.008 6.280 0.000 Cons 2.789 0.771 3.618 0.000 Note: I explicitly correlated the residuals of IQ and LW and obtained: LW WITH IQ -2.412 1.638 -1.472 0.141 (this residual covariance is not different from zero) Also, the model is just-identified, just as in ivreg2: TESTS OF MODEL FIT Chi-Square Test of Model Fit Value 0.000 Degrees of Freedom 0 P-Value 0.0000These estimates are pretty much the same as the ivreg2 estimates fromStata.Now, when I constrain the covariance between the two error terms of the endogenous variables to be to be zero, I have what I termed "an overidentifying restriction": TESTS OF MODEL FIT Chi-Square Test of Model Fit Value 2.914 Degrees of Freedom 1 P-Value 0.0878 This test is is about the same as your endog test: Endogeneity test of endogenous regressors: 2.909 Chi-sq(1) P-val = 0.0881 Thus, in this case, the test cannot reject the null.* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: re: ivreg2: No validity tests if just-identified?***From:*Kit Baum <baum@bc.edu>

- Prev by Date:
**Re: st: Dynamic count data panels** - Next by Date:
**Re: st: re: ivreg2: No validity tests if just-identified?** - Previous by thread:
**Re: st: re: ivreg2: No validity tests if just-identified?** - Next by thread:
**Re: st: re: ivreg2: No validity tests if just-identified?** - Index(es):

© Copyright 1996–2014 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |