[R-SIG-Finance] R: [Fwd: R-SIG-Finance Digest, Vol 60, Issue 18]
Melo Velandia Luis Fernando
Lmelovel at banrep.gov.co
Mon May 18 22:04:54 CEST 2009
It seems that in STATA your are using a robust covariance-matrix
estimators meanwhile in R not. This might explain the difference in the
Hausman tests. I am not an expert in the PLM package, but there is a
function "vcovHC" which uses a robust covariance matrix estimator
(White). You might try this.
Sincerely,
Luis
-----Mensaje original-----
De: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] En nombre de Steven
Archambault
Enviado el: Lunes, 18 de Mayo de 2009 01:27 p.m.
Para: Millo Giovanni
CC: r-sig-finance at stat.math.ethz.ch; Yves Croissant; Christian Kleiber
Asunto: Re: [R-SIG-Finance] R: [Fwd: R-SIG-Finance Digest, Vol 60, Issue
18]
Giovani,
Thank you so much for your comments. I am a bit new to R, and to these
mailing lists, so I apologize for being sparse on the details and
examples.
I am using Stata 9.2, which might be the answer to my problem, as you
described. I have done quite a bit of internet searching, and did not
read anywhere about the use of a different method for calculating the
chi-sq value, so thanks for that.
One more issue I have been thinking about. I am assuming your Plm
package knows that the FE is the consistient model, as the same results
arrive if the code is phtest(femod, remod) or phtest(remod, femod). The
order does matter in Stata.
For complteness I am going to post my results using the same Grumfeld
dataset for both stata 9.2 (by hand calculation and canned procedure)
and R. I am using the Plm package version 1 1-2.
Regards,
Steve
## begin Stata9.2 output##
xtreg inv value capital, robust re;
Random-effects GLS regression Number of obs =
200
Group variable (i): firmid Number of groups =
10
R-sq: within = 0.7668 Obs per group: min =
20
between = 0.8196 avg =
20.0
overall = 0.8061 max =
20
Random effects u_i ~ Gaussian Wald chi2(3) =
77.70
corr(u_i, X) = 0 (assumed) Prob > chi2 =
0.0000
------------------------------------------------------------------------
------
| Robust
invest | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------
-------------+------
value | .1097811 .0197587 5.56 0.000 .0710547
.1485076
capital | .308113 .0418387 7.36 0.000 .2261107
.3901153
_cons | -57.83441 24.67795 -2.34 0.019 -106.2023
-9.466507
-------------+----------------------------------------------------------
-------------+------
sigma_u | 84.20095
sigma_e | 52.767964
rho | .71800838 (fraction of variance due to u_i)
------------------------------------------------------------------------
------
. matrix bfe=e(b);
. matrix vfe=e(V);
. estimates store remod;
. xtreg inv value capital, robust fe;
Fixed-effects (within) regression Number of obs =
200
Group variable (i): firmid Number of groups =
10
R-sq: within = 0.7668 Obs per group: min =
20
between = 0.8194 avg =
20.0
overall = 0.8060 max =
20
F(2,188) =
40.23
corr(u_i, Xb) = -0.1517 Prob > F =
0.0000
------------------------------------------------------------------------
------
| Robust
invest | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+----------------------------------------------------------
-------------+------
value | .1101238 .019378 5.68 0.000 .0718975
.1483501
capital | .3100653 .042795 7.25 0.000 .2256452
.3944854
_cons | -58.74393 23.37422 -2.51 0.013 -104.8534
-12.63449
-------------+----------------------------------------------------------
-------------+------
sigma_u | 85.732501
sigma_e | 52.767964
rho | .72525012 (fraction of variance due to u_i)
------------------------------------------------------------------------
------
###Hausman by hand###
. estimates store femod;
. matrix vre=e(V);
. matrix bre=e(b);
. matrix bdif=bfe-bre;
. matrix list bdif;
bdif[1,3]
value capital _cons
y1 -.00034265 -.00195236 .90952273
. matrix bdifp=bdif';
. matrix dv=vfe-vre;
. matrix dvi=inv(dv);
. matrix list bdif;
bdif[1,3]
value capital _cons
y1 -.00034265 -.00195236 .90952273
. matrix list bdifp;
bdifp[3,1]
y1
value -.00034265
capital -.00195236
_cons .90952273
. matrix list dvi;
symmetric dvi[3,3]
value capital _cons
value -7739.3615
capital 5808.2905 -5305.811
_cons 3.6641311 .98569198 -.00051157
. matrix chisq=bdif*dvi*bdifp;
. matrix list chisq;
symmetric chisq[1,1]
y1
y1 -.01956929
###Hausman canned###
. hausman femod remod;
---- Coefficients ----
| (b) (B) (b-B)
sqrt(diag(V_b-V_B))
| femod remod Difference S.E.
-------------+----------------------------------------------------------
-------------+------
value | .1101238 .1097811 .0003427 .
capital | .3100653 .308113 .0019524 .0089965
------------------------------------------------------------------------
------
b = consistent under Ho and Ha; obtained from
xtreg
B = inconsistent under Ha, efficient under Ho; obtained from
xtreg
Test: Ho: difference in coefficients not systematic
chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= -0.01 chi2<0 ==> model fitted on these
data fails to meet the
asymptotic
assumptions of the Hausman test;
see suest for a generalized test
## end Stata9.2 output ##
##begin Output R, using PLM 1.1-2###
> test<-data(Grunfeld, package="Ecdat")
>
> fm <- inv~value+capital
> femod <- plm(fm, Grunfeld, model="within")
> summary(femod)
Oneway (individual) effect Within Model
Call:
plm(formula = fm, data = Grunfeld, model = "within")
Balanced Panel: n=10, T=20, N=200
Residuals :
Min. 1st Qu. Median 3rd Qu. Max.
-184.000 -17.600 0.563 19.200 251.000
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
value 0.110124 0.011857 9.2879 < 2.2e-16 ***
capital 0.310065 0.017355 17.8666 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 2244400
Residual Sum of Squares: 523480
F-statistic: 309.014 on 2 and 188 DF, p-value: < 2.22e-16
> remod <- plm(fm, Grunfeld, model="random")
> summary(remod)
Oneway (individual) effect Random Effect Model
(Swamy-Arora's transformation)
Call:
plm(formula = fm, data = Grunfeld, model = "random")
Balanced Panel: n=10, T=20, N=200
Effects:
var std.dev share
idiosyncratic 2784.458 52.768 0.282
individual 7089.800 84.201 0.718
theta: 0.86122
Residuals :
Min. 1st Qu. Median 3rd Qu. Max.
-178.00 -19.70 4.69 19.50 253.00
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
(Intercept) -57.834415 28.898935 -2.0013 0.04536 *
value 0.109781 0.010493 10.4627 < 2e-16 ***
capital 0.308113 0.017180 17.9339 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 2381400
Residual Sum of Squares: 548900
F-statistic: 328.837 on 2 and 197 DF, p-value: < 2.22e-16
> phtest(femod, remod)
Hausman Test
data: fm
chisq = 2.3304, df = 2, p-value = 0.3119 alternative hypothesis: one
model is inconsistent
###end Plm###
On Mon, May 18, 2009 at 6:01 AM, Millo Giovanni
<Giovanni_Millo at generali.com
> wrote:
> Dear Steve,
>
> I got your inquiry courtesy of Christian Kleiber, who brought it to
> our
> attention: please next time you post anything re a given package,
> include the maintainer's address. We cannot guarantee to parse all the
> daily digests of the R system!
>
> Your problem: can you please provide a reproducible example? Else it
> is difficult to help, not knowing your data, your results and even the
> Stata version you're using.
>
> In the following I replicate what you might have done on a well-known
> dataset.
>
> From Stata10, on the usual Grunfeld data taken from package "Ecdat":
>
> ## begin Stata10 output ##
> . xtreg inv value capital
>
> Random-effects GLS regression Number of obs =
> 200
> Group variable: firm Number of groups =
> 10
>
> R-sq: within = 0.7668 Obs per group: min =
> 20
> between = 0.8196 avg =
> 20.0
> overall = 0.8061 max =
> 20
>
> Random effects u_i ~ Gaussian Wald chi2(2) =
> 657.67
> corr(u_i, X) = 0 (assumed) Prob > chi2 =
> 0.0000
>
> ----------------------------------------------------------------------
> --
> ------
> inv | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------
> -------------+--
> ------
> value | .1097811 .0104927 10.46 0.000 .0892159
> .1303464
> capital | .308113 .0171805 17.93 0.000 .2744399
> .3417861
> _cons | -57.83441 28.89893 -2.00 0.045 -114.4753
> -1.193537
> -------------+--------------------------------------------------------
> -------------+--
> ------
> sigma_u | 84.20095
> sigma_e | 52.767964
> rho | .71800838 (fraction of variance due to u_i)
> ----------------------------------------------------------------------
> --
> ------
>
> . estimates store remod
>
> . xtreg inv value capital, fe
>
> Fixed-effects (within) regression Number of obs =
> 200
> Group variable: firm Number of groups =
> 10
>
> R-sq: within = 0.7668 Obs per group: min =
> 20
> between = 0.8194 avg =
> 20.0
> overall = 0.8060 max =
> 20
>
> F(2,188) =
> 309.01
> corr(u_i, Xb) = -0.1517 Prob > F =
> 0.0000
>
> ----------------------------------------------------------------------
> --
> ------
> inv | Coef. Std. Err. t P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------
> -------------+--
> ------
> value | .1101238 .0118567 9.29 0.000 .0867345
> .1335131
> capital | .3100653 .0173545 17.87 0.000 .2758308
> .3442999
> _cons | -58.74393 12.45369 -4.72 0.000 -83.31086
> -34.177
> -------------+--------------------------------------------------------
> -------------+--
> ------
> sigma_u | 85.732501
> sigma_e | 52.767964
> rho | .72525012 (fraction of variance due to u_i)
> ----------------------------------------------------------------------
> --
> ------
> F test that all u_i=0: F(9, 188) = 49.18 Prob > F
=
> 0.0000
>
> . estimates store femod
>
> . hausman femod remod
>
> ---- Coefficients ----
> | (b) (B) (b-B)
> sqrt(diag(V_b-V_B))
> | femod remod Difference S.E.
> -------------+--------------------------------------------------------
> -------------+--
> ------
> value | .1101238 .1097811 .0003427 .0055213
> capital | .3100653 .308113 .0019524 .0024516
> ----------------------------------------------------------------------
> --
> ------
> b = consistent under Ho and Ha; obtained
> from xtreg
> B = inconsistent under Ha, efficient under Ho; obtained
> from xtreg
>
> Test: Ho: difference in coefficients not systematic
>
> chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B)
> = 2.33
> Prob>chi2 = 0.3119
>
> .
> ## end Stata10 output ##
>
> while from plm I get
>
> ## begin R putput ##
> > data(Grunfeld, package="Ecdat")
> > fm <- inv~value+capital
> >
> > femod <- plm(fm, Grunfeld)
> > remod <- plm(fm, Grunfeld, model="random")
> >
> > phtest(femod, remod)
>
> Hausman Test
>
> data: fm
> chisq = 2.3304, df = 2, p-value = 0.3119 alternative hypothesis: one
> model is inconsistent
>
> ## end R output ##
>
> which, besides testifying to the goodness and parsimony of an
> object-oriented approach as far as screen output is concerned, looks
> rather consistent to me.
>
> I cannot but guess that the problem might stem from different RE
> estimates: previous versions of Stata used the Wallace-Hussein method
> by default for computing the variance of random effects. Now Stata
> uses Swamy-Arora, which has been the default of 'plm' since the
beginning.
> Yet as plm() allows to choose, you can experiment with different
> values for the 'random.method' argument in order to see if you get the
> Stata result. I suggest you start by comparing the coefficient
> estimates you get from Stata and R: FE should be unambiguous, RE might
> vary as said above, and for good reason.
>
> You also didn't tell us whether your by-hand calculation agrees with
> phtest() output? (I guess it does not)
>
> Please let us know, possibly with a reproducible example and providing
> all the above info Giovanni
>
> PS please also make sure you're not using any VEEEEERY old version of
> 'plm' (prior to, say, 0.3): these had a bug in the p-value calculation
> which made it depend on the order of models compared (so that in the
> wrong case you got p.value=1).
>
> Giovanni Millo
> Research Dept.,
> Assicurazioni Generali SpA
> Via Machiavelli 4,
> 34132 Trieste (Italy)
> tel. +39 040 671184
> fax +39 040 671160
>
> > --------------------------------------------------------------------
> > --
> > --
> >
> > Subject:
> > [R-SIG-Finance] Chi-sq Hausman test---R vs Stata
> > From:
> > Steven Archambault <archstevej at gmail.com>
> > Date:
> > Sun, 17 May 2009 23:14:13 -0600
> > To:
> > r-sig-finance at stat.math.ethz.ch
> >
> > To:
> > r-sig-finance at stat.math.ethz.ch
> >
> >
> > Hi all,
> >
> > I am running a panel time series regression testing Fixed Effects
> > and Random Effects. I decided to calculate the chi-sq value for the
> > Hausman test in both R (Phtest) and Stata. I get different results.
> > Even within Stata, calculating the Chi-sq value with the canned
> > procedure or by hand (using
> > matrices) gives different results. So, the question should come up
> there as
> > well.
> >
> > Does anybody have any insight on how to pick which results to use? I
> > guess the one that gives the result I want? Having different
> > programs give quite different values for the same tests is
> > frustrating me. I'd
>
> > be interested in any feedback folks have!
> >
> > Thanks,
> > Steve
> >
> > [[alternative HTML version deleted]]
>
[[alternative HTML version deleted]]
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