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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 17:19:23 -0400
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
>>
>> *
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>> * 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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--
PhD Candidate, Business Economics,
Stephen M. Ross School of Business,
University of Michigan, Ann Arbor.
http://sitemaker.umich.edu/sbagchi/home
*
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