Thanks for the help Kit! It allowed me to change a bit of the program
and almost have it correct. The program now seems to is the following:
program mymlprog
version 10.0
args lnf mu1 mu2 sigma
quietly replace `lnf' = log(1/sqrt(2*_pi*`sigma'^2))-
(1/(2*`sigma'^2))*($ML_y1+`mu1')^2 if $ML_y1 <0
quietly replace `lnf' =log(normal((`mu2')/`sigma')-
normal((`mu1')/`sigma')) if $ML_y1 == 0
quietly replace `lnf' =log(1/sqrt(2*_pi*`sigma'^2))-
(1/(2*`sigma'^2))*($ML_y1+`mu2')^2 if $ML_y1 > 0
end
ml model lf mymlprog (mu1: ret=mktret) (mu2: ret=mktret) /sigma
ml check
ml maximize
Although the results are very similar to the ones I get in Excel, they
aren't quite the same because the beta coefficients different in the
two equations. However, I need them to be the same... Is there a way
of forcing Stata to produce these results?
Best,
B.
On Dec 8, 2007 4:19 PM, Kit Baum <[email protected]> wrote:
> Ben wrote
>
> The program I've written in Stata is the following
> (which, for obvious reasons, isn't working):
>
> program mymlprog
> version 10.0
> args y x
>
> quietly replace `y' = ln(1/sqrt(2*_pi*Sigma_hat^2))-
> ((1/(2*Sigma_hat^2))*(`y'+Alpha_1-Beta_hat*`x')^2 if $ML_y1 <0
>
> quietly replace `y' =
> ln(normal((Alpha_2-Beta_hat*`x')/Sigma_hat)-normal((Alpha_1-
> Beta_hat*`x')/Sigma_hat))
> if $ML_y1 == 0
>
> quietly replace `y' =
> ln[1/sqrt(2*_pi*Sigma_hat^2)]-(1/(2*Sigma_hat^2))*(`y'+Alpha_2-
> Beta_hat*`x')^2
> if $ML_y1 > 0
>
> end
>
>
> I think this will do it. Note that there are some fundamental
> conceptual flaws in what Ben has written above. This can be viewed as
> a linear-form (lf) model of a rather strange sort (given that there
> are two parameter vectors that share an element). I think that could
> be handled by applying constraints that the two slope coefficients
> should be the same.
>
> --------mymlprog.ado--------
> program mymlprog
> version 10.0
> args lnf mu1 mu2
>
> quietly replace `lnf' = log(1/sqrt(2*_pi*$Sigma_hat^2))- ///
> (1/(2*$Sigma_hat^2))*($ML_y1+`mu1')^2 if $ML_y1 <0
>
> quietly replace `lnf' =log(normal((`mu2')/$Sigma_hat)- ///
> normal((`mu1')/$Sigma_hat)) if $ML_y1 == 0
>
> quietly replace `lnf' =log(1/sqrt(2*_pi*$Sigma_hat^2))- ///
> (1/(2*$Sigma_hat^2))*($ML_y1+`mu2')^2 ///
> if $ML_y1 > 0
>
> end
> ---------end of program------
>
> to run, you must first set the global value of Sigma_hat to some
> value (as that does not seem to be a parameter to be estimated)
>
> global Sigma_hat 10
>
> I created a dataset with the desired characteristics with
>
> webuse auto
> g mpgzap = mpg - 25
> ml model lf mymlprog (mpgzap = price) (mpgzap=price)
> ml check
> ml maximize
>
>
> Number of obs
> = 74
> Wald chi2(1)
> = 4.76
> Log likelihood = -243.23578 Prob > chi2
> = 0.0292
>
> ------------------------------------------------------------------------
> ------
> | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------
> +----------------------------------------------------------------
> eq1 |
> price | .0008808 .0004039 2.18 0.029 .
> 0000891 .0016725
> _cons | -2.170081 2.78924 -0.78 0.437
> -7.63689 3.296728
> -------------
> +----------------------------------------------------------------
> eq2 |
> price | .0050802 .0023531 2.16 0.031 .
> 0004683 .0096922
> _cons | -19.44901 10.88796 -1.79 0.074
> -40.78903 1.891009
> ------------------------------------------------------------------------
> ------
>
> If we now do
>
> cons 1 [eq1]price = [eq2]price
> ml model lf mymlprog (mpgzap = price) (mpgzap=price) , constraints(1)
> ml maximize
>
> the two slope parameters will be equated;
>
> Number of obs
> = 74
> Wald chi2(0)
> = .
> Log likelihood = -246.37235 Prob > chi2
> = .
>
> ( 1) [eq1]price - [eq2]price = 0
> ------------------------------------------------------------------------
> ------
> | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------
> +----------------------------------------------------------------
> eq1 |
> price | .0010458 .0004001 2.61 0.009 .
> 0002615 .0018301
> _cons | -3.476855 2.776426 -1.25 0.210
> -8.918549 1.964839
> -------------
> +----------------------------------------------------------------
> eq2 |
> price | .0010458 .0004001 2.61 0.009 .
> 0002615 .0018301
> _cons | -.181225 2.8025 -0.06 0.948
> -5.674024 5.311574
> ------------------------------------------------------------------------
> ------
>
> I think this is estimating the model that Ben has specified. As he
> has an independent
> estimate of the model via Excel Solver, it should be easy to check
> that out.
>
> Kit
>
> Kit Baum, Boston College Economics and DIW Berlin
> http://ideas.repec.org/e/pba1.html
> An Introduction to Modern Econometrics Using Stata:
> http://www.stata-press.com/books/imeus.html
>
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
