Re: st: Computing the proportion of significant variables after running numerous regressions

[Nick Cox <njcoxstata@gmail.com>]

You seem to be guessing that after -bootstrap: regress- there is a
quantity left in memory called -_ci_bc_cons-. Not so. Also, each
confidence interval is a pair of numbers, so you need to create two
variables to hold it, not one. The trick to these calculations is to
see what is left in memory after a command. By the way, 10
replications would not be enough for most serious work.

* load dataset
 sysuse auto, clear

 * set up temporary file for results
 tempfile results
 tempname postfile
 postfile `postfile' foreign _b_cons _se_cons _b_mpg _se_mpg _cons_ll
_cons_ul _b_ll _b_ul using "`results'"

 * run bootstrapped regression for each level of foreign
 set seed 1 // so that you can repeat your analysis
 levelsof foreign, local(levels)
 foreach level of local levels {
        bootstrap, rep(10): regress price mpg if foreign==`level'
		mat ci = e(ci_bc)
        post `postfile' (`level') (_b[_cons]) (_se[_cons]) (_b[mpg])
(_se[mpg]) (ci[1,2]) (ci[2,2]) (ci[1,1]) (ci[2,1])
 }
 postclose `postfile'

 * display results
 use "`results'", clear
 list


On Mon, May 14, 2012 at 9:30 AM, George Murray
<george.murray16@gmail.com> wrote:
> Phil,
>
> Thank you so much for your help, this worked perfectly.
>
> I have one more query, however.
>
> I also need a vector of the bias-corrected confidence intervals (which
> can be obtained with the -estat bootstrap- command). I replace two of
> the commands you suggested with these two commands as follows:
>
> -postfile `postfile' foreign _b_cons _se_cons _ci_bc_cons _b_mpg
> _se_mpg using "`results'"- .............(all I did was add
> "_ci_bc_cons")
>
> -post `postfile' (`level') (_b[_cons]) (_se[_cons]) (_ci_bc[_cons])
> (_b[mpg]) (_se[mpg])- .............(all I did was add
> "(_ci_bc[_cons])")
>
> and I also wrote -estat boostrap- after the bootstrap, rep(10)... command
>
> However, I get the following error:
>
> _ci_bc not found
> post:  above message corresponds to expression 3, variable _ci_bc_cons
> r(111);
>
> Does anyone know how to solve this problem?


On Mon, May 14, 2012 at 12:05 AM, Phil Clayton
> <philclayton@internode.on.net> wrote:
>> George,
>>
>> There are various ways to do this. One is to use -post- after each bootstrapped regression to store the results of that regression in a "results" dataset, similar to a Monte Carlo simulation. You can then access the results dataset and manipulate it however you like.
>>
>> Here's a basic example that uses the auto dataset and loops over the levels of "foreign" (ie 0 and 1), runs a bootstrapped regression of price on mpg for each level, and displays the resulting coefficients and standard errors.
>>
>> --------- begin example ---------
>> * load dataset
>> sysuse auto, clear
>>
>> * set up temporary file for results
>> tempfile results
>> tempname postfile
>> postfile `postfile' foreign _b_cons _se_cons _b_mpg _se_mpg using "`results'"
>>
>> * run bootstrapped regression for each level of foreign
>> set seed 1 // so that you can repeat your analysis
>> levelsof foreign, local(levels)
>> foreach level of local levels {
>>        bootstrap, rep(10): regress price mpg if foreign==`level'
>>        post `postfile' (`level') (_b[_cons]) (_se[_cons]) (_b[mpg]) (_se[mpg])
>> }
>> postclose `postfile'
>>
>> * display results
>> use "`results'", clear
>> list
>> --------- end example ---------
>>
>> Since you're running ~1000 models you may wish to change "foreach" to "qui foreach", and monitor the iterations using the _dots command (see Harrison DA. Stata tip 41: Monitoring loop iterations. Stata Journal 2007;7(1):140, available at http://www.stata-journal.com/article.html?article=pr0030)
>>
>> Phil
>>
>>
>> On 13/05/2012, at 10:06 PM, George Murray wrote:
>>
>>> Dear Statalist,
>>>
>>> I am using the -foreach- command to run approximately 1000
>>> (bootstrapped) regression models, however I require an efficient way
>>> of calculating the proportion of the regression models which have a
>>> statistically significant constant at the 5% level; and of the
>>> constants which are statistically significant, the proportion which
>>> are positive.  Below each of the 1000 regressions I run, a table is
>>> displayed with the following format:
>>>
>>> ---------------------------------------------------------------------------------------------------
>>>             |    Observed                         Bootstrap
>>>        V0 |       Coef.             Bias         Std. Err.
>>> [95% Conf. Interval]
>>> -------------+------------------------------------------------------------------------------------
>>>         V1 |   .00968169  -.0000537   .00057051     .008721   .0111218  (BC)
>>>         V2 |  -.00110469   .0000782     .000691   -.0023101    .000459  (BC)
>>>         V3 |   .00468313  -.0001562   .00084971    .0031954   .0064538  (BC)
>>>         _cons |  -.00076976   .0001811   .00176677   -.0044496   .0025584  (BC)
>>> --------------------------------------------------------------------------------------------------
>>>
>>> I would be *very* grateful if someone knew the commands which would
>>> allow me calculate this. In the past, I have used (a highly tedious
>>> and embarrassing approach on) Excel where I filtered every Nth row,
>>> and wrote a command to display 1 if the coefficient lies within the
>>> confidence interval, and 0 if not. This time, however, I am running
>>> numerous models and require a quicker approach.
>>>
>>> One more question -- is there a way to create a new variable where the
>>> coefficients of V1 (for example) are saved, so I can calculate the
>>> mean, standard deviation etc.of V1?
>>>
>>> If someone could answer at least one of these two questions, it would
>>> be very much appreciated.

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