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From |
Helene Ehrhart <Helene.Ehrhart@u-clermont1.fr> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: 2SLS with random effects correcting for autocorrelation |

Date |
Thu, 26 Mar 2009 10:19:38 +0100 |

Thank you Nicola for these helpful suggestions. I'll try them! Hélène. Quoting nicola.baldini2@unibo.it:

No suggestions that 100% fit your problem.Among second best solutions, you may leave the random effects. Insuch a case, an uncommon choice is to try the -fmivreg- fromhttp://www.antonisureda.com/other/stuff/files/ which uses the Famaand MacBeth (1973) two step procedure, with instrumental variablesand Newey-West standard errors. In the first step, for each singletime period a cross-sectional regression is performed. Then, in thesecond step, the final coefficient estimates are obtained as theaverage of the first step coefficient estimates.Fama, Eugene F., and James D. MacBeth, 1973. Risk, Return, andEquilibrium: Empirical tests, Journal of Political Economy 81,607-636.Other options (not involving the fixed effects) are firstdifferencing (-xitvreg2- and -xtabond2-) or cross-sectional commands(-ivreg2-, -newey2-), all of them are available from SSC.Nicola P.S. I'll NOT receive/read any email but the Digest. At 02.33 21/03/2009 -0400, Helene Ehrhart wrote:Dear all, I would like to estimate an equation with instrumental variables using random effects and correcting for autocorrelation. I already tried many ways to do that in stata but no command was able to meet all the three requirements : - - xtivreg , re does not allow for autocorrelation correction - - xtivreg2 does not allow for random effects - - xtdata to transform data so that it corresponds to random effects and then estimation with ivreg28 but then the option bw(1) is not possible since the data are transformed - - estimating the first stage with xtreg ,re ; taking the predicted dependant value and using it as regressors for the 2nd stage using xtregar ,re which corrects for autocorrelation. This works but the standard errors should then be corrected by bootstrapping to correct the bias of using a predicted value as regressor. Unfortunately, traditional bootstrap using a random sample does not maintain the autocorrelation structure. So this method should also be eliminated. If anyone already faced this problem, estimating in 2SLS with random effects correcting for autocorrelation, I would really appreciate to know what is the proper way to do that in Stata. Thanks, Hélène Ehrhart.

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**Follow-Ups**:**Re: st: 2SLS with random effects correcting for autocorrelation***From:*Stephen Armah <armah.stephen@gmail.com>

**References**:**Re: st: 2SLS with random effects correcting for autocorrelation***From:*nicola.baldini2@unibo.it

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