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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Robust instrumental variable regression |

Date |
Fri, 14 Jan 2011 12:52:51 -0600 |

On Fri, Jan 14, 2011 at 6:09 AM, Ramiro H. Gálvez <ramirogalvez@gmail.com> wrote: > I am using stata 11 and I'm having problems with outliers in a 2SLS > instrumental variable regression. Is there any implementation in stata > equivalent to rreg for instrumental variable regression (like rivregress)? > Can anyone tell me of some bibliography on the subject? Finally, it's > correct to make an "riveg" (robust ivreg), by making the first and second > estimation in a 2SLS regression by myself using rreg? Conceptually, what you would need for robust instrumental variables regression is a robust version of E[ error times instrument ] = 0 That this expression can be obtained, via a nice geometry of the least squares, with a two stage procedure is an unhappy coincident that provokes incorrect analogies -- I've no clue what the two-stage procedure with -rreg- at each stage would really mean, and what the parameter estimates may converge to in large samples. There's probably little you can do about the instrument in the above expectation, but there are probably different ways to approach the error term there. 1. You can use median[ error times instrument ] = 0. To code this, you would need to work with some sort of a simplex algorithm, as is done in -qreg-. An outlying value of (error times instrument) may be due to a large error with a typical value of the instrument; an influential point on the instrument scale with small error; or moderately high values of both. Whether that gives you the control over the outliers is up to you. 2. You can use E[ g(error) times instrument] = 0 where g() is a bounded influence function that you have learned about in your robust statistics course. (If you have not had any, it will all be gibberish to you, but you won't be able to even put a finger to the concept of robustness unless you know what the breakdown point and the influence function are.) You can probably code this with the g() of your choice using -gmm-, either in the interactive version with -instruments()-, or in the evaluator form. Whether there are any references behind my suggestions, I have no clue -- I am just thinking aloud here. You can probably represent either of them within the framework of estimating equations to demonstrate asymptotic normality and applicability of the usual -_robust- standard errors. -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * 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**:**st: Robust instrumental variable regression***From:*"Ramiro H. Gálvez" <ramirogalvez@gmail.com>

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