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From |
Xiang Ao <[email protected]> |

To |
"[email protected]" <[email protected]> |

Subject |
Re: st: RE: overidentification test after treatreg |

Date |
Fri, 22 Oct 2010 13:06:28 -0400 |

Also, do you have any idea what went wrong with my gmm codes? Thanks, Xiang On 10/22/2010 11:19 AM, Schaffer, Mark E wrote:

Xiang,`-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Xiang Ao Sent: 22 October 2010 15:10 To: [email protected] Subject: st: overidentification test after treatreg Dear Statalisters, I have a question on how to do a Sargan's test after treatreg. I found Mark Schaffer's comments on this question from 2006: http://www.stata.com/statalist/archive/2006-08/msg00804.html In the reply, Mark suggested using a LR test between a full model with all instruments in the second stage and a regular treatreg. My question is: this only tests the hypothesis that all excluded instruments jointly being zero, how would that tell us the robustness of instruments, as Sargan's test would do in an ivreg setting? Mark kindly replied to my email to him and suggested posting to statalist to get more inputs. I am thinking of using gmm to frame the treatreg problem, then Jansen's J would be a byproduct. However, my code with gmm does not generate consistent estimates with treatreg, which I am sure is due to my lack of knowledge on this. I post my code here; any suggestion is greatly appreciated. sysuse auto, clear global xb "{b1}*gear_ratio + {b2}*length + {b3}*headroom + {b0}" global phi "normalden($xb)" global Phi "normal($xb)" global lambda "foreign*$phi/$Phi - (1-foreign)*$phi/(1-$Phi)" global xb2 "{c1}*gear_ratio + {c2}*length + {c3}*headroom + {c0} + {c5}*foreign" gmm (eq1: $lambda) (eq2: turn-$xb2), instruments(eq1: gear_ratio length headroom mpg) instruments(eq2: gear_ratio length headroom foreign ) winitial(unadjusted, independent) wmatrix(unadjusted) This is to try to estimate the same model as: treatreg turn gear_ratio length headroom, treat(foreign=gear_ratio length headroom mpg)`

Here was my rationale for how to do an overid test using an LR statistic. As I wrote it in that Statalist post from 2006 that you cite, I think I got it wrong. Here's my next attempt: Consider a slightly simplified version of your treatreg model: treatreg turn, treat(foreign=mpg) There are two overidentifying restrictions. First, mpg appears in the treatment equation (foreign) but not in the outcome equation (turn). Second, normality is also an identifying restriction, much in the same way as normality can be used in a Heckman selection model as an identifying restriction. Now consider your treatreg model, but with mpg as a regressor in the outcome equation: treatreg turn mpg, treat(foreign=mpg) This second version is just-identified, with normality as the sole identifying restriction. So, the following should be an LR test of the overidentifying restrctions in your original model: treatreg turn, treat(foreign=mpg) est store troverid treatreg turn mpg, treat(foreign=mpg) est store trjustid lrtest troverid trjustid, df(1) I should also note that this is a system test. The overidentified system is (pardon the terrible shorthand notation): turn = a + b*foreign + c*mpg foreign = d + e*mpg The just-identified system is turn = a + b*foreign foreign = d + e*mpg And your overid test is an LR test of c=0. I *think* this is right, but perhaps you or others on the list could comment. Cheers, MarkBut they don't match. Thank you for your time, Xiang * * 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/

**Follow-Ups**:**RE: st: RE: overidentification test after treatreg***From:*"Schaffer, Mark E" <[email protected]>

**References**:**st: overidentification test after treatreg***From:*Xiang Ao <[email protected]>

**st: RE: overidentification test after treatreg***From:*"Schaffer, Mark E" <[email protected]>

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