Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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/
>>
>>
>> -----
>> 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



-- 
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/


© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index