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Re: st: Re: hypothesis tests


From   "Dr. Stephen Owusu-Ansah" <sowusuansah@rgv.rr.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Re: hypothesis tests
Date   Mon, 05 Jan 2004 10:52:22 -0600

Christopher F Baum wrote:

On Jan 5, 2004, at 2:33 AM, Stephen wrote:

I follow your analysis. But I have a question: Are you implying that
if I use say "test" in place of the "lrtest" command it will produce
a Wald chi-square test statistic instead of Likelihood-ratio test statistic?

Now concerning my comment on "mvreg" and Wald test, I was making a
reference to Stata manuals' statement that the "testparm" (a varaint of
the "test" command) can be used after all estimation commands except
"anova" for single equations, and "mlogit" or "mvreg" for
multiple-equations.

With regard to the first q, yes and no; if you use an estimator that generates t-statistics in the coefficient listings, -test- will generate a Wald F-test. If you use an estimator that generates z-statistics in the coefficient listings (e.g. ivreg2 by default generates z-stats), -test- will generate a Wald Chi-squared statistic. Stata considers that small-sample standard errors should provide an F, and large-sample standard errors should provide a Chi^2.

-whelp testparm- shows that testparm may indeed be used for -mvreg- (see the description of equation(eqno) and that -testanova- is appropriate after -anova-. That help file also specifies that -test- and -testparm- perform Wald tests, and -help testnl- indicate that "Wald-type" tests are performed via the delta method; it follows the same rule regarding F- and Chi-squared statistics.

Kit
I followed the syntax you previously suggested in estimating the constrained and unconstrained models, but used the "test" command
with "regress" estimator instead of the "lrtest" command. It returned
with an error message (see below). What am I doing wrong? I am using
version 8.

. constr def 1 e_invpro=c_invpro

. cnsreg mve asst prosal e_invpro c_invpro, c(1)

Constrained linear regression Number of obs = 168
F( 3, 164) = 64.55
Prob > F = 0.0000
Root MSE = 4.6e+08
( 1) e_invpro - c_invpro = 0
------------------------------------------------------------------------
mve | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------
asst | .3962174 .0307523 12.88 0.000 .3354959 .4569389
prosal | 2.136713 1.923157 1.11 0.268 -1.660627 5.934052
e_invpro | .4515937 .1274536 3.54 0.001 .1999322 .7032552
c_invpro | .4515937 .1274536 3.54 0.001 .1999322 .7032552
_cons | 8.60e+07 4.17e+07 2.06 0.041 3711166 1.68e+08
------------------------------------------------------------------------

. est store constr

. reg mve asst prosal e_invpro c_invpro

Source | SS df MS Number of obs = 168
-------------+------------------------------ F( 4, 163) = 48.43
Model | 4.0232e+19 4 1.0058e+19 Prob > F = 0.0000
Residual | 3.3854e+19 163 2.0769e+17 R-squared = 0.5430
-------------+------------------------------ Adj R-squared = 0.5318
Total | 7.4086e+19 167 4.4363e+17 Root MSE = 4.6e+08

------------------------------------------------------------------------
mve | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------
asst | .3910566 .0315464 12.40 0.000 .3287644 .4533488
prosal | 2.391729 1.955249 1.22 0.223 -1.469154 6.252612
e_invpro | .5930094 .2270513 2.61 0.010 .1446684 1.04135
c_invpro | .401676 .1438101 2.79 0.006 .117705 .6856471
_cons | 8.25e+07 4.20e+07 1.97 0.051 -335028.8 1.65e+08
------------------------------------------------------------------------

. test constr, force
option force not allowed
r(198);


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