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st: RE: First-stage F from -xtivreg- versus AP F
From
"Schaffer, Mark E" <[email protected]>
To
"[email protected]" <[email protected]>
Subject
st: RE: First-stage F from -xtivreg- versus AP F
Date
Tue, 21 Jan 2014 14:54:51 +0000
Diego,
In your case you have a single endogenous regressor, which means the standard diagnostic first-stage F and the Angrist-Pischke first-stage F are the same number.
So if you wade through the output of -xtoverid,noi-, you'll find this under the first-stage estimation:
> F test of excluded instruments:
> F( 2, 27825) = 3528.57
> Prob> F = 0.0000
> Angrist-Pischke multivariate F test of excluded instruments:
> F( 2, 27825) = 3528.57
> Prob> F = 0.0000
And then in the main regression output of -xtoverid,noi-, where the -xtivreg- results are replicated, you'll find this:
> Weak identification test
> Ho: equation is weakly identified
> Cragg-Donald Wald F statistic 3528.57
and this:
> Weak identification test (Cragg-Donald Wald F statistic): 3528.573
So your first-stage F stat is 3528.57. Looks a bit large to me, but that's the number you are looking for.
HTH,
Mark
> -----Original Message-----
> From: [email protected] [mailto:owner-
> [email protected]] On Behalf Of David Torres
> Sent: 21 January 2014 14:29
> To: [email protected]
> Subject: st: First-stage F from -xtivreg- versus AP F
>
> Statalisters,
>
> I'm using -xtivreg- to estimate a 2SLS random effects model in Stata 13.0. I'm
> needing help getting the first stage F statistic in addition to the Angrist-
> Pischke F I can get from the postestimation command -xtoverid- (more on
> this below).
>
> . which xtivreg
> /Applications/Stata/ado/base/x/xtivreg.ado
> *! version 1.6.2 14apr2011
>
> . which xtoverid
> /Users/diego/Library/Application Support/Stata/ado/plus/x/xtoverid.ado
> *! xtoverid version 2.1.6 2Nov2011
> *! Authors Mark Schaffer and Steve Stillman
> *! Derived from overidxt and overid
>
>
> Here is the most basic conditional model in which I am regressing vertically
> scaled reading scores on year and year-squared. The endogenous regressor,
> diffsch, denotes whether a student attended an out-of-zone magnet school.
> It is instrumented with the log difference in distance between the zoned
> school and the nearest magnet school and the log distance to the enrolled
> school:
>
> . xtivreg srsc year yearsq (diffsch=diffdist dist_enrld), re first
>
>
> First-stage G2SLS regression
> Number of obs = 27830
> Wald chi(4) = 7182
> Prob> chi2 = 0.0000
> ------------------------------------------------------------------------------
> diffsch | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> year | .0010897 .0023365 0.47 0.641 -.0034898 .0056692
> yearsq | .0009441 .0005764 1.64 0.101 -.0001858 .0020739
> diffdist | .004433 .001686 2.63 0.009 .0011284 .0077376
> dist_enrld | .162999 .0019443 83.84 0.000 .1591883 .1668097
> _cons | .1364031 .0035329 38.61 0.000 .1294787 .1433274
> ------------------------------------------------------------------------------
>
> G2SLS random-effects IV regression Number of obs = 27830
> Group variable: short_id Number of groups = 6168
>
> R-sq: within = 0.8770 Obs per group: min = 3
> between = 0.2215 avg = 4.5
> overall = 0.6533 max = 5
>
> Wald chi2(3) = 154192.94
> corr(u_i, X) = 0 (assumed) Prob> chi2 = 0.0000
>
> ------------------------------------------------------------------------------
> srsc | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> diffsch | 9.999284 1.941415 5.15 0.000 6.19418 13.80439
> year | 74.19692 .3811099 194.69 0.000 73.44996 74.94389
> yearsq | -7.926549 .0940278 -84.30 0.000 -8.11084 -7.742258
> _cons | 475.1334 .6555358 724.80 0.000 473.8486 476.4182
> -------------+----------------------------------------------------------------
> sigma_u | 37.264026
> sigma_e | 25.567877
> rho | .67991546 (fraction of variance due to u_i)
> ------------------------------------------------------------------------------
> Instrumented: diffsch
> Instruments: year yearsq diffdist dist_enrld
> ------------------------------------------------------------------------------
>
>
> Now I would like to get the first stage F-statistic, but the first option above
> does not appear to give it. If I use the postestimation command -xtoverid-
> with the noisily option, as shown below, I do get the Angrist-Pischke F, but in
> my write-up I'd like to show the regular F-stat to test joint significance of the
> instruments.
>
> . xtoverid, nois
>
> First-stage regressions
> -----------------------
>
> First-stage regression of __00000I:
>
> OLS estimation
> --------------
>
> Estimates efficient for homoskedasticity only
> Statistics consistent for homoskedasticity only
>
> Number of obs = 27830
> F( 5, 27825) = 2097.56
> Prob> F = 0.0000
> Total (centered) SS = 875.5555503 Centered R2 = 0.2037
> Total (uncentered) SS = 960.0415225 Uncentered R2 = 0.2737
> Residual SS = 697.2383726 Root MSE = .1583
>
> ------------------------------------------------------------------------------
> __00000I | Coef. Std. Err. t P>|t| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> __00000R | .0010897 .0023365 0.47 0.641 -.00349 .0056694
> __00000U | .0009441 .0005764 1.64 0.101 -.0001858 .0020739
> __00000E | .1364031 .0035329 38.61 0.000 .1294784 .1433277
> __00000L | .004433 .001686 2.63 0.009 .0011283 .0077377
> __00000O | .162999 .0019443 83.84 0.000 .1591881 .1668098
> ------------------------------------------------------------------------------
> Included instruments: __00000R __00000U __00000E __00000L __00000O
> ------------------------------------------------------------------------------
> F test of excluded instruments:
> F( 2, 27825) = 3528.57
> Prob> F = 0.0000
> Angrist-Pischke multivariate F test of excluded instruments:
> F( 2, 27825) = 3528.57
> Prob> F = 0.0000
>
>
>
> Summary results for first-stage regressions
> -------------------------------------------
>
> (Underid) (Weak id)
> Variable | F( 2, 27825) P-val | AP Chi-sq( 2) P-val | AP F( 2, 27825)
> __00000I | 3528.57 0.0000 | 7058.41 0.0000 | 3528.57
>
> Stock-Yogo weak ID test critical values for single endogenous regressor:
> 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.
>
> Underidentification test
> Ho: matrix of reduced form coefficients has rank=K1-1 (underidentified)
> Ha: matrix has rank=K1 (identified)
> Anderson canon. corr. LM statistic Chi-sq(2)=5630.40 P-val=0.0000
>
> Weak identification test
> Ho: equation is weakly identified
> Cragg-Donald Wald F statistic 3528.57
>
> Stock-Yogo weak ID test critical values for K1=1 and L1=2:
> 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.
>
> Weak-instrument-robust inference
> Tests of joint significance of endogenous regressors B1 in main equation
> Ho: B1=0 and orthogonality conditions are valid
> Anderson-Rubin Wald test F(2,27825)= 13.53 P-val=0.0000
> Anderson-Rubin Wald test Chi-sq(2)= 27.06 P-val=0.0000
> Stock-Wright LM S statistic Chi-sq(2)= 27.03 P-val=0.0000
>
> Number of observations N = 27830
> Number of regressors K = 4
> Number of endogenous regressors K1 = 1
> Number of instruments L = 5
> Number of excluded instruments L1 = 2
>
> IV (2SLS) estimation
> --------------------
>
> Estimates efficient for homoskedasticity only
> Statistics consistent for homoskedasticity only
>
> Number of obs = 27830
> F( 4, 27826) = 3.6e+05
> Prob> F = 0.0000
> Total (centered) SS = 122387579.2 Centered R2 = 0.8485
> Total (uncentered) SS = 976762125.4 Uncentered R2 = 0.9810
> Residual SS = 18546537.84 Root MSE = 25.82
>
> ------------------------------------------------------------------------------
> __00000G | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> __00000I | 9.999284 1.941276 5.15 0.000 6.194454 13.80412
> __00000R | 74.19692 .3810826 194.70 0.000 73.45002 74.94383
> __00000U | -7.926549 .094021 -84.31 0.000 -8.110827 -7.742271
> __00000E | 475.1334 .6554887 724.85 0.000 473.8487 476.4181
> ------------------------------------------------------------------------------
> Underidentification test (Anderson canon. corr. LM statistic): 5630.398
> Chi-sq(2) P-val = 0.0000
> ------------------------------------------------------------------------------
> Weak identification test (Cragg-Donald Wald F statistic): 3528.573
> 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.
> ------------------------------------------------------------------------------
> Sargan statistic (overidentification test of all instruments): 0.577
> Chi-sq(1) P-val = 0.4474
> ------------------------------------------------------------------------------
> Instrumented: __00000I
> Included instruments: __00000R __00000U __00000E
> Excluded instruments: __00000L __00000O
> ------------------------------------------------------------------------------
>
> Test of overidentifying restrictions:
> Cross-section time-series model: xtivreg g2sls
> Sargan-Hansen statistic 0.577 Chi-sq(1) P-value = 0.4474
>
>
> Can anyone tell me how to get what I want, the regular first stage F-stat,
> from all this? I'd very much appreciate your help.
>
> Thanks for your consideration, folks,
> Diego Torres
> *
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