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From |
John Antonakis <john.antonakis@unil.ch> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: AW: Simulating stepwise regression |

Date |
Fri, 07 Aug 2009 18:44:47 +0200 |

Thanks Tirthankar!

Best, J. ____________________________________________________ Prof. John Antonakis Associate Dean Faculty of Business and Economics University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 Faculty page: http://www.hec.unil.ch/people/jantonakis&cl=en Personal page: http://www.hec.unil.ch/jantonakis ____________________________________________________ On 07.08.2009 13:10, Tirthankar Chakravarty wrote:

You should probably use -simulate-. Here is what it might look like: *********************************** capture program drop sim version 10 program define sim, rclass drop _all syntax , nreg(integer ) nobs(integer ) set obs `nobs' forv i=1/`nreg' { g x`i' = invnormal(uniform()) } gen y = invnorm(uniform()) stepwise, pr(.2): regress y x* qui indeplist return scalar r2d2 = e(r2) end /* simulate for each of the regressor and sample size combinations required. 10,000 replications. */ foreach nobs of numlist 1000 1500 2000 { forv nreg = 1(1)10 { simulate r2d2=r(r2d2), reps(10000) /// saving(sw_r2_`nobs'_`nreg'.dta, every(1) /// replace) seed(123): sim, nreg(`nreg') /// nobs(`nobs') } } use sw_r2_1000_5, clear kdensity r2d2 *********************************************** On Fri, Aug 7, 2009 at 11:18 AM, John Antonakis<john.antonakis@unil.ch> wrote:That's very helpful; thanks Martin. To extend the below, how would I simulate the r-square? That is, I want to run the simulation say 100 times, and then obtain the mean r-square from each simulation. Thus, I can show, at a specific sample size (n=100) and number of independent variables (k=5), what the r-square would be just by chance alone. As an extension, is there a way to vary the sample size (n from 50 to 1000, in increments of 50) and the number of independent variables (k=1 to k=100 in increments of 1) in the simulation? Best, J. ____________________________________________________ Prof. John Antonakis Associate Dean Faculty of Business and Economics University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 Faculty page: http://www.hec.unil.ch/people/jantonakis&cl=en Personal page: http://www.hec.unil.ch/jantonakis ____________________________________________________ On 07.08.2009 12:06, Martin Weiss wrote:<> You could also -tokenize- the return from -indeplist- and have your -program- return the regressors one by one... ************* capt prog drop sim version 10.1 program define sim, rclass drop _all set obs 100 gen y = invnorm(uniform()) gen x1 = invnorm(uniform()) gen x2 = invnorm(uniform()) gen x3 = invnorm(uniform()) gen x4 = invnorm(uniform()) gen x5 = invnorm(uniform()) stepwise, pr(.2): regress y x1-x5 qui indeplist tokenize "`r(X)'" ret loc one="`1'" ret loc two="`2'" ret loc three="`3'" ret loc four="`4'" ret loc five="`5'" end sim ret li ************* HTH Martin -----Ursprüngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von John Antonakis Gesendet: Freitag, 7. August 2009 11:47 An: statalist@hsphsun2.harvard.edu Betreff: st: Simulating stepwise regression Hi: I would like to simulate the below. Note, I am no fan of stepwise--I just want to demonstrate it evils However, I do not know 1. what to put in the place of "??"--that is, I want the program to capture only the variables that were selected in the model as being significant 2. how to simulate the r-square. 3. how to extend the simulation (a new program) such that I simulate from n = 50 to n=1000 (in increments of 50), crossed with independent variables ranging from x1 to x100. Regards, John. Here is the program: set seed 123456 capture program drop sim version 10.1 program define sim, eclass drop _all set obs 100 gen y = invnorm(uniform()) gen x1 = invnorm(uniform()) gen x2 = invnorm(uniform()) gen x3 = invnorm(uniform()) gen x4 = invnorm(uniform()) gen x5 = invnorm(uniform()) stepwise, pr(.2): regress y x1-x5 end simulate ??? , reps(20) seed (123) : sim, foreach v in ?? { gen t_`v' = /* */_b_`v'/_se_`v' gen p_`v' =/* */ 2*(1-normal(abs(t_`v'))) } ____________________________________________________ Prof. John Antonakis Associate Dean Faculty of Business and Economics University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 Faculty page: http://www.hec.unil.ch/people/jantonakis&cl=en Personal page: http://www.hec.unil.ch/jantonakis ____________________________________________________ * * 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/* * 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/

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**Follow-Ups**:**Re: st: AW: Simulating stepwise regression***From:*Tirthankar Chakravarty <tirthankar.chakravarty@gmail.com>

**References**:**Re: Re: st: Panel data regression***From:*Andreas Hatzigeorgiou <anha@umich.edu>

**st: Simulating stepwise regression***From:*John Antonakis <john.antonakis@unil.ch>

**Re: st: AW: Simulating stepwise regression***From:*John Antonakis <john.antonakis@unil.ch>

**Re: st: AW: Simulating stepwise regression***From:*Tirthankar Chakravarty <tirthankar.chakravarty@gmail.com>

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