Re: st: Maximum likelihood estimation

[Joseph Monte <hmjc66@gmail.com>]

Thanks Brian. Sorry for the reposts. I'll try to be clearer next time.

Joe


On Wed, Feb 13, 2013 at 10:00 PM, Brian P. Poi <bpoi@stata.com> wrote:
> On 02/13/2013 03:27 PM, Joseph Monte wrote:
>>
>> Dear Statalisters,
>>
>> I'll try again with a little more info since I did not get any
>> responses. Here is the code I have so far (based on previous Statalist
>> posts and "Maximum Likelihood estimation with Stata" by
>> Gould, Pitbaldo and Poi, 4th ed.). The paper I cited in my first email
>> below models the log of the variance of the regression error in
>> equation 2 while I believe I have modelled the log of sigma. I would
>> preferably like to model the log of the variance as in the paper cited
>> but am not sure how.
>>
>> cscript
>>    program mynormal_lf1
>>            version 12
>>                    args todo b lnfj g1 g2
>>            tempvar mu lnsigma sigma
>>                    mleval `mu' = `b', eq(1)
>>                    mleval `lnsigma' = `b', eq(2)
>>            quietly {
>>                                  gen double `sigma' = exp(`lnsigma')
>>                                  replace `lnfj' =
>> ln(normalden($ML_y1,`mu',`sigma'))
>>                                  if (`todo'==0) exit
>>                                  tempvar z
>>                                  tempname dmu dlnsigma
>>                                  gen double `z' = ($ML_y1-`mu')/`sigma'
>>                                  replace `g1' = `z'/`sigma'
>>                                  replace `g2' = `z'*`z'-1
>>                  }
>>    end
>>
>>
>> ml model lf1 mynormal_lf1 (mu: y = x1 x2 x3 x4 x5 x6 x7 x8 x9)
>> (lnsigma: y = x1 x2 x3 x4 x5 x6 x7 x8 x9)
>> ml max
>>
>> Thanks,
>>
>> Joe
>>
>>
>
> Joe,
>
> Letting Stata do the work of computing numerical derivatives, here is the
> equivalent program that models the log variance rather than the log standard
> deviation:
>
> -------------
> cscript
>
> program mynormal_lf0
>         version 12
>         args todo b lnfj
>         tempvar mu lnvar var
>         mleval `mu' = `b', eq(1)
>         mleval `lnvar' = `b', eq(2)
>         quietly {
>                 gen double `var' = exp(`lnvar')
>                 replace `lnfj' =  ln(normalden($ML_y1,`mu',sqrt(`var')))
>         }
> end
>
> ml model lf0 mynormal_lf0 (mu: y = x1 x2 x3 x4 x5 x6 x7 x8 x9)          ///
>         (lnvar: y = x1 x2 x3 x4 x5 x6 x7 x8 x9)
> ml max
> -------------
>
> The only real change is that since the normalden() function's third argument
> is the standard deviation, not the variance, we need to take the square root
> of the variance.  By construction, the variance variable `var' will be
> non-negative, so taking the square root is not a problem here.
>
>    -- Brian Poi
>    -- bpoi@stata.com
>
>
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