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RE: st: RE: Results of overidentification and underidentification test missing
From
"Schaffer, Mark E" <[email protected]>
To
"[email protected]" <[email protected]>
Subject
RE: st: RE: Results of overidentification and underidentification test missing
Date
Mon, 23 Sep 2013 09:23:52 +0000
Sutirtha,
What about ivreg2? (That's the one I asked about - xtivreg2 is basically a wrapper for ivreg2.)
Even if it looks up to date, you might try forcing an update - it will reinstall the Mata library livreg2.mlib, which has also been updated in the meantime (but the date of which you can't easily check).
--Mark
> -----Original Message-----
> From: [email protected] [mailto:owner-
> [email protected]] On Behalf Of Sutirtha Bagchi
> Sent: 22 September 2013 22:19
> To: [email protected]
> Subject: Re: st: RE: Results of overidentification and underidentification test
> missing
>
> Hello Mark,
>
> Thanks for responding. This is what I have:
>
> xtivreg2: xtivreg2 1.0.13 28Aug2011
>
> ranktest: ranktest 1.1.02 15oct2007
>
> Thanks,
> Sutirtha
>
> On Sun, Sep 22, 2013 at 1:55 PM, Schaffer, Mark E <[email protected]>
> wrote:
> > Sutirtha,
> >
> > Can you also tell us what versions of ivreg2 and ranktest you have installed?
> xtivreg2 uses these programs.
> >
> > --Mark
> >
> >> -----Original Message-----
> >> From: [email protected] [mailto:owner-
> >> [email protected]] On Behalf Of Sutirtha Bagchi
> >> Sent: 21 September 2013 22:56
> >> To: [email protected]
> >> Subject: st: Results of overidentification and underidentification
> >> test missing
> >>
> >> Hello,
> >>
> >> I am using the user-written command -xtivreg2- in Stata11 (Stata/SE
> >> 11.2 for Windows (32-bit)).
> >>
> >> (*! xtivreg2 1.0.13 28Aug2011 *! author mes)
> >>
> >> The issue I am facing is that in the Stata output, I find the results
> >> of the Under identification and Weak Identification test missing. In
> >> particular, the Kleibergen-Paap rk LM statistic and associated
> >> p-value and the Kleibergen-Paap rk Wald F statistic are missing.
> >> Other test statistics such as the Hansen J statistic for overidentification
> and the Shea partial R2 are present in the output.
> >> I can verify that I have updated Stata and so that alone is unlikely
> >> to fix this issue for me.
> >>
> >> Here are details of my data set on municipal pension plans where
> >> this comes up.
> >>
> >> I have one observation per pension plan per municipality per time
> >> period (decade). For simplicity, let us say, I have 2 pension plans
> >> per municipality for ~
> >> 1,000 municipalities for 3 decades - a total of
> >> 2 X 1,000 X 3 or ~ 6,000 observations. I am looking at the effect of
> >> political orientation of the municipality (more specifically, the
> >> independent variable is average Democratic vote share in mayoral
> >> elections held in the last decade) on a measure of funding for the pension
> plans offered by that municipality.
> >> However, I am concerned about the possible endogeneity of the
> >> independent variable and I therefore use demographic characteristics
> >> (percent of the population that is self-employed and percent of the
> >> population that has a
> >> disability) as instruments for the independent variable. As it turns
> >> out, Democratic vote share goes up when the percent of the
> >> population that is self- employed goes down or when the percent of
> >> the population that has a disability goes up.
> >>
> >> The Stata command I use is:
> >>
> >> xi: xtivreg2 wmeanactfundratio_emplgrp2 (average_share_dems_votes7
> =
> >> pctslfemplydownbiznotincp pctpop16to64wdisability) i.currentdecade,
> >> fe gmm2s first cluster(county)
> >>
> >> where wmeanactfundratio_emplgrp2 = Mean funding ratio of pension
> plan
> >> offered by a municipality for a particular employee group (with the
> >> mean being taken over a decade);
> >> average_share_dems_votes7 = Average Democratic vote share for
> mayoral
> >> races held in the last decade; pctslfemployedownbiznotincp = Percent
> >> of the population that is self-employed; pctpop16to64wdisability =
> >> Percent of the population between 16 to 64 that has a disability;
> >> i.currentdecade is a set of dummy variables for the decade; and
> >> finally, county - These 1,000 municipalities can belong to one of ~
> >> 65 counties. Clustering standard errors at the county level is the most
> conservative and so I go with that.
> >>
> >>
> >> Here is the output:
> >>
> >> Warning - singleton groups detected. 117 observation(s) not used.
> >> FIXED EFFECTS ESTIMATION
> >>
> >> ------------------------
> >>
> >> Number of groups = 1135 Obs per group: min = 2
> >>
> >> avg = 4.6
> >>
> >> max = 9
> >>
> >> First-stage regressions
> >>
> >> -----------------------
> >>
> >> First-stage regression of average_share_dems_votes7:
> >>
> >> FIXED EFFECTS ESTIMATION
> >>
> >> ------------------------
> >>
> >> Number of groups = 1135 Obs per group: min = 2
> >>
> >> avg = 4.6
> >>
> >> max = 9
> >>
> >> OLS estimation
> >>
> >> --------------
> >>
> >> Estimates efficient for homoskedasticity only
> >>
> >> Statistics robust to heteroskedasticity and clustering on county
> >>
> >> Number of clusters (county) = 65 Number of obs = 5253
> >>
> >>
> >> F( 4, 64) = 8.95
> >>
> >>
> >> Prob > F = 0.0000
> >>
> >> Total (centered) SS = 9.605866911 Centered R2 = 0.2599
> >> Total (uncentered) SS = 9.605866911 Uncentered R2 = 0.2599
> >> Residual SS = 7.109186342 Root MSE = .04157
> >>
> >>
> >> ---------------------------------------------------------------------
> >> ---------
> >>
> >> | Robust
> >>
> >> average_s~s7 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
> >>
> >> -------------+-------------------------------------------------------
> >> -------------+---
> >> -------------+------
> >>
> >> _Icurre~1990 | .0355722 .0077302 4.60 0.000 .0201294 .051015
> >>
> >> _Icurre~2000 | .0432069 .0122536 3.53 0.001 .0187274 .0676864
> >>
> >> pctslfempl~p | -.0001385 .0007944 -0.17 0.862 -.0017255 .0014485
> >>
> >> pctpop16to~y | .0040835 .0013014 3.14 0.003 .0014837 .0066832
> >>
> >> ---------------------------------------------------------------------
> >> ---------
> >>
> >> Included instruments: _Icurrentde_1990 _Icurrentde_2000
> >>
> >> pctslfemplydownbiznotincp pctpop16to64wdisability
> >>
> >> ------------------------------------------------------------------------------
> >> Partial R-squared of excluded instruments: 0.0327
> >> Test of excluded instruments:
> >> F( 2, 64) = 5.50
> >> Prob > F = 0.0062
> >>
> >> Summary results for first-stage regressions
> >> -------------------------------------------
> >>
> >> Variable | Shea Partial R2 | Partial R2 | F( 2, 64) P-value
> >>
> >> average_shar| 0.0327 | 0.0327 | 5.50 0.0062
> >>
> >> NB: first-stage F-stat cluster-robust
> >>
> >> Underidentification tests
> >>
> >> Ho: matrix of reduced form coefficients has rank=K1-1
> >> (underidentified)
> >>
> >> Ha: matrix has rank=K1 (identified)
> >>
> >> Kleibergen-Paap rk LM statistic Chi-sq(2)=. P-val= .
> >>
> >> Kleibergen-Paap rk Wald statistic Chi-sq(2)=. P-val= .
> >>
> >> Weak identification test
> >>
> >> Ho: equation is weakly identified
> >>
> >> Kleibergen-Paap Wald rk F statistic .
> >>
> >> See main output for Cragg-Donald weak id test critical values
> >>
> >> Weak-instrument-robust inference
> >>
> >> Tests of joint significance of endogenous regressors B1 in main
> >> equation
> >>
> >> Ho: B1=0 and overidentifying restrictions are valid
> >>
> >> Anderson-Rubin Wald test F(2,64)= 0.92 P-val=0.4038
> >>
> >> Anderson-Rubin Wald test Chi-sq(2)=1.87 P-val=0.3927
> >>
> >> Stock-Wright LM S statistic Chi-sq(2)=1.87 P-val=0.3927
> >>
> >> NB: Underidentification, weak identification and
> >> weak-identification-robust test statistics cluster-robust
> >>
> >> Number of clusters N_clust = 65
> >>
> >> Number of observations N = 5253
> >>
> >> Number of regressors K = 3
> >>
> >> Number of instruments L = 4
> >>
> >> Number of excluded instruments L1 = 2
> >>
> >> 2-Step GMM estimation
> >>
> >> ---------------------
> >>
> >> Estimates efficient for arbitrary heteroskedasticity and clustering
> >> on county
> >>
> >> Statistics robust to heteroskedasticity and clustering on county
> >>
> >> Number of clusters (county) = 65 Number of obs = 5253
> >>
> >> F( 3, 64) = 14.86
> >>
> >> Prob > F = 0.0000
> >>
> >> Total (centered) SS = 29430177.08 Centered R2 = 0.0334
> >>
> >> Total (uncentered) SS = 29430177.08 Uncentered R2 = 0.0334
> >>
> >> Residual SS = 28447688.07 Root MSE = 83.12
> >>
> >>
> >> ---------------------------------------------------------------------
> >> ---------
> >>
> >> | Robust
> >>
> >> wmeanactfu~2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> >>
> >> -------------+-------------------------------------------------------
> >> -------------+---
> >> -------------+------
> >>
> >> average_s~s7 | 209.2071 159.7796 1.31 0.190 -103.9552 522.3693
> >>
> >> _Icurre~1990 | -30.10698 9.738608 -3.09 0.002 -49.19431 -11.01966
> >>
> >> _Icurre~2000 | -47.3599 11.90716 -3.98 0.000 -70.6975 -24.0223
> >>
> >> ---------------------------------------------------------------------
> >> ---------
> >>
> >> Underidentification test (Kleibergen-Paap rk LM statistic): .
> >>
> >> Chi-sq(2) P-val = .
> >>
> >> ---------------------------------------------------------------------
> >> ---------
> >>
> >> Weak identification test (Kleibergen-Paap rk Wald F statistic): .
> >>
> >> Stock-Yogo weak ID test critical values: 10% maximal IV size 19.93
> >>
> >> 15% maximal IV size 11.59
> >>
> >> 20% maximal IV size 8.75
> >>
> >> 25% maximal IV size 7.25
> >>
> >> Source: Stock-Yogo (2005). Reproduced by permission.
> >>
> >> NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
> >>
> >> ---------------------------------------------------------------------
> >> ---------
> >>
> >> Hansen J statistic (overidentification test of all instruments): 0.017
> >>
> >>
> >> Chi-sq(1) P-val = 0.8959
> >>
> >> ---------------------------------------------------------------------
> >> ---------
> >>
> >> Instrumented: average_share_dems_votes7
> >>
> >> Included instruments: _Icurrentde_1990 _Icurrentde_2000
> >>
> >> Excluded instruments: pctslfemplydownbiznotincp
> >> pctpop16to64wdisability
> >>
> >> ---------------------------------------------------------------------
> >> ---------
> >>
> >> Please let me know if you need any further details. Thanks for any
> >> and all suggestions,
> >>
> >> Sutirtha Bagchi
> >>
> >> *
> >> * 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/
>
>
>
> --
> PhD Candidate, Business Economics,
> Stephen M. Ross School of Business,
> University of Michigan, Ann Arbor.
> http://sitemaker.umich.edu/sbagchi/home
> *
> * 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/