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Re: st: RE: Results of overidentification and underidentification test missing
From
Sutirtha Bagchi <[email protected]>
To
[email protected]
Subject
Re: st: RE: Results of overidentification and underidentification test missing
Date
Sun, 22 Sep 2013 19:26:37 -0400
I did have a chance to update xtivreg2 and ranktest to the most
current version and that solved the problem. The versions I now have
are:
. which xtivreg2, all
c:\ado\plus\x\xtivreg2.ado
*! xtivreg2 1.0.13 28Aug2011
*! author mes
. which ranktest, all
c:\ado\plus\r\ranktest.ado
*! ranktest 1.3.02 27Feb2012
*! author mes, based on code by fk
*! see end of file for version comments
Thanks,
Sutirtha
On Sun, Sep 22, 2013 at 5:19 PM, Sutirtha Bagchi <[email protected]> wrote:
> 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/
>>
>>
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>>
>> *
>> * For searches and help try:
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>
>
>
> --
> PhD Candidate, Business Economics,
> Stephen M. Ross School of Business,
> University of Michigan, Ann Arbor.
> http://sitemaker.umich.edu/sbagchi/home
--
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/