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Re: st: RE: Hausman Test Problems
From
John Antonakis <[email protected]>
To
[email protected]
Subject
Re: st: RE: Hausman Test Problems
Date
Mon, 02 May 2011 12:34:11 +0200
Right; thanks for the clarification, Mark. Maybe I was not clear enough,
but if one model is nested in the other, then one makes a restriction
(that the L2 predictors do not correlate with the constant effect),
which gives the overid interpretation. The reason why I talked about
this is that I thought that Muhammad was thinking that he need to have
instruments (where he said that he did not "pretest it for
overidentification").
BTW, the paper I cited below is a real eye opener because it show just
how many restrictions are in these models (for those who are interested):
Bollen, K. A.,& Brand, J. E. (2010). A General Panel Model with Random and Fixed Effects A Structural Equations
Approach. Social Forces, 89(1), 1-34.
Best,
J.
__________________________________________
Prof. John Antonakis
Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis
Associate Editor
The Leadership Quarterly
__________________________________________
On 02.05.2011 11:45, Schaffer, Mark E wrote:
John, Muhammad,
The test of fixed vs. random effects (also) has an overid test
interpretation. The FE estimator uses the moment conditions
E(x_it*e_it)=0. The RE estimator uses, in addition, the moment
conditions E(x_it*u_i)=0. That's what makes it overidentified and an
overid test possible.
There is a short discussion and some references in the xtoverid help
file.
Cheers,
Mark
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of
John Antonakis
Sent: 02 May 2011 10:11
To: [email protected]
Subject: Re: st: RE: Hausman Test Problems
You don't need to be overidentified to use xtoverid. The
command in fact tests a constraint that is made, which nests
the random and fixed-effects models (i.e., the constraint
that is made to the random effects model is that level 2
regressors do not correate with uj).
To get a better handle on what types of constraints are made
in these types of models see:
Bollen, K. A.,& Brand, J. E. (2010). A General Panel Model
with Random and Fixed Effects A Structural Equations
Approach. Social Forces, 89(1), 1-34.
HTH,
John.
__________________________________________
Prof. John Antonakis
Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis
Associate Editor
The Leadership Quarterly
__________________________________________
On 02.05.2011 10:56, Muhammad Anees wrote:
Thanks Eric!
It did worked for me. I actually run the regressions without
pretesting it for any overidentification. Can I still follow any
procedure selecting one of the FE and RE using over
identified panel
data regressions.
On 2 May 2011 12:44, DE SOUZA
Eric<[email protected]> wrote:
The Hausman test for fixed vs random is only valid under
a strict set of assumptions. These assumptions are clearly
not satisfied in your case .
Use -xtoverid-. Download it from ssc: -ssc install
xtoverid- and read the help file first.
Eric de Souza
College of Europe
Brugge (Bruges), Belgium
http://www.coleurope.eu
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of
Muhammad
Anees
Sent: 02 May 2011 06:12
To: [email protected]
Subject: st: Hausman Test Problems
Dear All!
I have run a panel data regression and selection of the
random effects or fixed effects using Hausman test. I do not
know what is the actual problem with my results. Please could
someone help. Why the result for my hausman command results
in warning message?
the complete results are below:
. xtreg priceclose eps bookvalue, fe
Fixed-effects (within) regression Number of
obs = 850
Group variable: id Number of
groups = 170
R-sq: within = 0.1160 Obs per
group: min = 5
between = 0.5266
avg = 5.0
overall = 0.4645
max = 5
F(2,678) = 44.48
corr(u_i, Xb) = 0.4836 Prob> F
= 0.0000
priceclose Coef. Std. Err. t P>t [95%
Conf. Interval]
eps .7770481 .1966364 3.95 0.000 .3909585
1.163138
bookvalue .8653121 .1577343 5.49 0.000
.5556057 1.175018
_cons 1.001173 .1176642 8.51 0.000 .7701434
1.232204
sigma_u 3.5662704
sigma_e 1.5953308
rho .83325562 (fraction of variance due to u_i)
F test that all u_i=0: F(169, 678) = 17.34
Prob> F = 0.0000
.
. estimates store fe
.
. xtreg priceclose eps bookvalue, re
Random-effects GLS regression Number of
obs = 850
Group variable: id Number of
groups = 170
R-sq: within = 0.1159 Obs per
group: min = 5
between = 0.5186
avg = 5.0
overall = 0.4593
max = 5
Random effects u_i ~ Gaussian Wald
chi2(2) = 297.79
corr(u_i, X) = 0 (assumed) Prob>
chi2 = 0.0000
priceclose Coef. Std. Err. z P>z [95%
Conf. Interval]
eps 1.113035 .2084971 5.34 0.000 .7043883
1.521682
bookvalue 1.394302 .1196459 11.65 0.000
1.159801 1.628804
_cons .5629992 .2070207 2.72 0.007 .1572462
.9687522
sigma_u 2.1242726
sigma_e 1.5953308
rho .63938518 (fraction of variance due to u_i)
.
. estimates store re
.
. hausman fe re
---- Coefficients ----
(b) (B) (b-B) sqrt(diag(V_b-V_B))
fe re Difference S.E.
eps .7770481 1.113035 -.3359869 .
bookvalue .8653121 1.394302 -.5289903 .102786
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)
= -15.59 chi2<0 ==> model fitted on these
data fails to meet the asymptotic
assumptions of the Hausman test;
see suest for a generalized test
--
Muhammad Anees
MSc in Economics
The University of Sheffield
United Kingdom
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