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From |
"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk> |

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

Subject |
st: RE: Re: About weakiv |

Date |
Thu, 19 Dec 2013 15:20:00 +0000 |

Tak Wai, That's interesting. Maybe try the F-stat version of AR? You can either calculate it from the reported chi-sq stat, or do the AR test by hand. If I understand your DGP correctly, the AR test will have an F distribution, so the F-stat version of the test should be properly sized. --Mark > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu [mailto:owner- > statalist@hsphsun2.harvard.edu] On Behalf Of Chau Tak Wai > Sent: 19 December 2013 14:14 > To: statalist@hsphsun2.harvard.edu > Subject: st: Re: About weakiv > > Mark, > > I have used the -small- option all the way. > > Thanks, > Tak Wai > > ======================== > > Tak Wai, > > Your simulation is set up with 100 obs. The AR test has many degrees of > freedom (you have 15 instruments), and I think you're using the large- > sample (chi-sq) version of the test stat. > > Maybe AR won't be so oversized if you use a small-sample correction? You > could try using the -small- option of -weakiv-, and maybe compare that to > doing the AR test by hand using an F test. > > HTH, > Mark > > > > Dear Mark and other Statalist users, > > > > Thanks a lot for Mark's reply. > > > > Indeed my aim is to see how good it performs under heteroscedasticity, > > and it should be robust even when the DGP is homoscedastic. > > > > When I remove the robust option of ivregress before weakiv, the > > following results are obtained: (I have used rho=0.9 here) > > > > clrtest 1000 .082 .274502 0 1 > > artest 1000 .166 .3722668 0 1 > > wtest 1000 .867 .3397446 0 1 > > > > Wald test is large, but it agrees with 2SLS. It is the case because > > it's highly overidentified (15 instruments and 1 endo regressor) with > > average first-stage F of 2. > > > > CLR test now looks OK (though a little over rejects) while AR test > > seriously over-rejects. It looks problematic as these tests are > > supposed to be robust to weak IV. > > > > I haven't checked about condivreg yet. > > > > Hope the information helps. > > > > Tak Wai > > > > P.S. Sorry, since I only subscribe the digest version, I cannot reply > > the email of the reply directly. > > ======== > > > > Tak Wai, > > > > Does the issue come up when you don't use the -robust- version? You > could double-check in that case against -condivreg-, which will report the CLR > stat but only for the iid case. > > > > If it's specific to -robust-, maybe have a look at the Finlay-Magnusson 2009 > SJ paper cited in the weakiv help file. They say at one point that: > > > > " For linear IV models under homoskedasticity, Andrews, Moreira, and > > Stock (2007) provide a formula for computing the p-value function of the > CLR test (which is embedded in the condivreg command). Although this is not > the correct p-value function when homoskedasticity is violated, our > simulations indicate that it provides a good approximation." > > > > --Mark > > > >> Dear Statalisters, > >> > >> I have a question about weakiv. I have tried to run some simulations > >> to understand the performance of CLR test. But in the following code > >> with > >> 15 instruments and 1 endogenous regressor, the result seems to be far > >> from what it should be. I cannot spot the error of my code. So I > >> would like to ask if it's the problem of my code or a bug in the procedure. > >> > >> I may have coded things badly, so advice is welcome! > >> > >> Thank you very much in advance! > >> > >> The code is as follows: > >> > >> capture program drop sim_iv_clr_15 > >> program define sim_iv_clr_15, rclass > >> version 11 > >> syntax [, obs(integer 100) mu2k(real 3) b(real 1) rho(real 0.5) ] > >> drop _all set obs `obs' > >> tempvar y x z1 z2 z3 z4 z5 z6 z7 z8 z9 z10 z11 z12 z13 z14 z15 u gen > >> `z1' = rnormal(0,1) gen `z2' = rnormal(0,1) gen `z3' = rnormal(0,1) > >> gen `z4' = rnormal(0,1) gen `z5' = rnormal(0,1) gen `z6' = > >> rnormal(0,1) gen `z7' = rnormal(0,1) gen `z8' = rnormal(0,1) gen `z9' > >> = rnormal(0,1) gen `z10' = rnormal(0,1) gen `z11' = rnormal(0,1) gen > >> `z12' = rnormal(0,1) gen `z13' = rnormal(0,1) gen `z14' = > >> rnormal(0,1) gen `z15' = rnormal(0,1) > >> > >> gen `u' = rnormal(0,1) > >> > >> gen `x' = > >> > sqrt(`mu2k'/`obs')*(`z1'+`z2'+`z3'+`z4'+`z5'+`z6'+`z7'+`z8'+`z9'+`z10'+`z11'+`z12 > '+`z13'+`z14'+`z15')+`u' > >> gen `y' = `b'*`x'+(`rho')*`u'+sqrt(1-`rho'^2)*rnormal(0,1) > >> > >> local z `z1' `z2' `z3' `z4' `z5' `z6' `z7' `z8' `z9' `z10' `z11' `z12' > >> `z13' `z14' `z15' > >> *check first stage > >> regress `x' `z' > >> return scalar fsf=e(F) > >> > >> ivregress 2sls `y' (`x' = `z'), robust return scalar beta2sls=_b[`x'] > >> return scalar se2sls=_se[`x'] > >> > >> ivregress liml `y' (`x' = `z'), robust return scalar betaliml=_b[`x'] > >> return scalar seliml=_se[`x'] > >> > >> ivreg2 `y' (`x'=`z' ), fuller(1) robust return scalar betaf1=_b[`x'] > >> return scalar sef1=_se[`x'] > >> > >> ** check the related tests at null of true value ivregress 2sls `y' > >> (`x' = `z'), robust weakiv, null(1.0) small return scalar > >> clrp=e(clr_p) return scalar arp=e(ar_p) return scalar wp=e(wald_p) > >> > >> **test at true value - 0.25 > >> ivregress 2sls `y' (`x' = `z'), robust weakiv, null(0.75) small > >> return scalar clrpa=e(clr_p) return scalar arpa=e(ar_p) return scalar > >> wpa=e(wald_p) > >> > >> **test at true value +0.25 > >> ivregress 2sls `y' (`x' = `z'), robust weakiv, null(1.25) small > >> return scalar clrpb=e(clr_p) return scalar arpb=e(ar_p) return scalar > >> wpb=e(wald_p) > >> > >> end > >> > >> local betatrue 1.0 > >> local nobs 100 > >> simulate fsf=r(fsf) beta2sls=r(beta2sls) se2sls=r(se2sls) /* */ > >> betaliml=r(betaliml) seliml=r(seliml) betaf1=r(betaf1) sef1=r(sef1)/* > >> */ clrp=r(clrp) arp=r(arp) wp=r(wp) clrpa=r(clrpa) arpa=r(arpa) > >> wpa=r(wpa) clrpb=r(clrpb) arpb=r(arpb) wpb=r(wpb), /* */ reps(1000): > >> sim_iv_clr_15, obs(`nobs') b(`betatrue') mu2k(1.0) rho(0.5) > >> > >> **I generate a dummy variable for rejection gen clrtest=(clrp<0.05) > >> gen artest=(arp<0.05) gen wtest=(wp<0.05) > >> > >> The results are then given by > >> For tests at true value > >> clrtest | 1000 .253 .4349485 0 1 > >> artest | 1000 .437 .4962633 0 1 > >> -------------+------------------------------------------------------- > >> -------------+- > >> wtest | 1000 .355 .4787528 0 1 > >> > >> For tests at true - 0.25 > >> clrtesta | 1000 .324 .4682342 0 1 > >> -------------+------------------------------------------------------- > >> -------------+- > >> artesta | 1000 .49 .5001501 0 1 > >> wtesta | 1000 .797 .4024338 0 1 > >> > >> For tests at true +0.25 > >> clrtestb | 1000 .351 .4775218 0 1 > >> artestb | 1000 .509 .5001691 0 1 > >> wtestb | 1000 .052 .2221381 0 1 > >> > >> So at least at the true value it is quite far from the nominal size of 0.05. > >> > >> Sincerely, > >> Tak Wai Chau > >> > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ ----- Sunday Times Scottish University of the Year 2011-2013 Top in the UK for student experience Fourth university in the UK and top in Scotland (National Student Survey 2012) We invite research leaders and ambitious early career researchers to join us in leading and driving research in key inter-disciplinary themes. Please see www.hw.ac.uk/researchleaders for further information and how to apply. Heriot-Watt University is a Scottish charity registered under charity number SC000278. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: About weakiv***From:*Chau Tak Wai <chau.takwai@gmail.com>

**st: Re: About weakiv***From:*Chau Tak Wai <chau.takwai@gmail.com>

**st: Re: About weakiv***From:*Chau Tak Wai <chau.takwai@gmail.com>

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