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st: Results of overidentification and underidentification test missing


From   Sutirtha Bagchi <[email protected]>
To   [email protected]
Subject   st: Results of overidentification and underidentification test missing
Date   Sat, 21 Sep 2013 17:56:29 -0400

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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