[Limdep Nlogit List] F-test question
William Greene
wgreene at stern.nyu.edu
Tue Apr 21 04:52:16 AEST 2020
Bill. It is not a feature. Try something like this:
create;fed=female*educ ; med=educ-fed ; male=1-female$$
regr;lhs=income;rhs=female,fed,male,med
;cls:b(1)-b(3)=0,b(2)-b(4)=0$$
regr;lhs=income;rhs=one,educ$
Please see below for results.
/Bill Greene
-----------------------------------------------------------------------------
Restricted least squares regression ............
LHS=INCOME Mean = .44476
Standard deviation = .21659
---------- No. of observations = 3377 DegFreedom Mean square
Regression Sum of Squares = 11.3623 1 11.36229
Residual Sum of Squares = 147.004 3375 .04356
Total Sum of Squares = 158.367 3376 .04691
---------- Standard error of e = .20870 Root MSE .20864
Fit R-squared = .07175 R-bar squared .07147
Model test F[ 1, 3375] = 260.86136 Prob F > F* .00000
Restrictions F[ 2, 3373] = 1.74597 Prob F > F* .17463
--------+--------------------------------------------------------------------
| Standard Prob. 95% Confidence
INCOME| Coefficient Error z |z|>Z* Interval
--------+--------------------------------------------------------------------
FEMALE| .16692*** .01757 9.50 .0000 .13248 .20136
FED| .02415*** .00150 16.15 .0000 .02122 .02708
MALE| .16692*** .01757 9.50 .0000 .13248 .20136
MED| .02415*** .00150 16.15 .0000 .02122 .02708
--------+--------------------------------------------------------------------
***, **, * ==> Significance at 1%, 5%, 10% level.
Model was estimated on Apr 20, 2020 at 02:47:40 PM
-----------------------------------------------------------------------------
|-> regr;lhs=income;rhs=one,educ$
-----------------------------------------------------------------------------
Ordinary least squares regression ............
LHS=INCOME Mean = .44476
Standard deviation = .21659
---------- No. of observations = 3377 DegFreedom Mean square
Regression Sum of Squares = 11.3623 1 11.36229
Residual Sum of Squares = 147.004 3375 .04356
Total Sum of Squares = 158.367 3376 .04691
---------- Standard error of e = .20870 Root MSE .20864
Fit R-squared = .07175 R-bar squared .07147
Model test F[ 1, 3375] = 260.86136 Prob F > F* .00000
--------+--------------------------------------------------------------------
| Standard Prob. 95% Confidence
INCOME| Coefficient Error z |z|>Z* Interval
--------+--------------------------------------------------------------------
Constant| .16692*** .01757 9.50 .0000 .13248 .20136
EDUC| .02415*** .00150 16.15 .0000 .02122 .02708
--------+--------------------------------------------------------------------
***, **, * ==> Significance at 1%, 5%, 10% level.
Model was estimated on Apr 20, 2020 at 02:49:10 PM
-----------------------------------------------------------------------------
On Mon, Apr 20, 2020 at 2:36 PM William Spitz <bills at gra-inc.com> wrote:
> I am doing an F-test for equality of coefficients in a simple linear model.
>
> Base model is y = a + b X
> I am testing whether the observations should be split into Groups 1 and 2:
> y = a1 + b1 X + a2 + b2 X
>
> But when I run the restricted model (using CLS: b(1)=b(3), b(2)=b(4)), I
> don't get the same restricted coefficient values as I do when running
> the Base model.
> They're pretty close, but not the same -- and it seems to be off by more
> than just rounding error.
>
> Is this a "feature" of imposing restrictions via CLS?
>
> Thanks in advance.
> /Bill Spitz
>
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--
William Greene
Department of Economics, emeritus
Stern School of Business, New York University
44 West 4 St.
New York, NY, 10012
URL: https://protect-au.mimecast.com/s/_m2lCmO5glu6YjJvfGdl1N?domain=people.stern.nyu.edu
Email: wgreene at stern.nyu.edu
Ph. +1.646.596.3296
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