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From |
Phil Clayton <philclayton@internode.on.net> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Mon, 14 May 2012 00:05:49 +1000 |

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. > > George Murray. > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Computing the proportion of significant variables after running numerous regressions***From:*George Murray <george.murray16@gmail.com>

**References**:**st: Computing the proportion of significant variables after running numerous regressions***From:*George Murray <george.murray16@gmail.com>

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