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From |
"Martin Weiss" <martin.weiss1@gmx.de> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: AW: Simulation in Stata |

Date |
Thu, 28 May 2009 09:16:27 +0200 |

<> Several things come to mind. Among them: You have to give explanations for the elements of your code, so that others know what the lines are there for. It is best to comment out via " /* */" so the comment can stretch across several lines. ************* expand 10 gen no = _n sort id_psu no drop no gen no = _n ************* is too complicated as you first have Stata record the current sort order via "_n" , then -sort- on "id_psu" and then -drop- "no" - only to recreate it in the next step. The line - scalar N = _N- is rarely needed as "_N" is available anytime you need it. Sometimes you have to turn it into an expression as `=_N' to get your hands on it. It is not a good idea to use the running index of your last -forvalues- loop as a -local- as well. Overall, why do you always insist on -matrices- when you can have variables? Also remember you have to put all of this into a -program- to allow -simulate- to do its magic... See -help simulate- at the bottom for a useful example. HTH Martin -----Ursprüngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Susan Olivia Gesendet: Donnerstag, 28. Mai 2009 01:29 An: statalist@hsphsun2.harvard.edu Betreff: st: Simulation in Stata Dear Stata list, I would like to run Monte Carlo simulation using artificially simulated autocorrelated data. In each experiment, the only variable that changes is the dependent variable (Y). Below is my programming attempt in creating the Y and am wondering whether there is more elegant way to implement my code in Stata. I am a beginner in programming and would appreciate any programming tips. Thanks, Susan // In this do file, the independent vars and the locations remain fixed for all iterations, only the dependent variable changes // clear local nrep 250 *qui set obs 100 *gen id_psu = _n tempvar sdalpha1 sdalpha2 gen `sdalpha1' = uniform() gen `sdalpha2' = uniform() * Expand to generate individual observations - here assuming that each cluster has 10 households * expand 10 gen no = _n sort id_psu no drop no gen no = _n gen xcoord = uniform()*100 gen ycoord = uniform()*100 spatwmat, name(W) xcoord(xcoord) ycoord(ycoord) band(0 100) eigenval(E) standardize scalar lambda = 0.5 gen constant = 5 mkmat constant scalar N = _N matrix Idtot = I(N) matrix IlambdaW = Idtot-(lambda*W) matrix invIlambdaW = inv(IlambdaW) * Generate the predictor & the variable y gen x = 5 + `sdalpha1'*invnorm(uniform()) + `sdalpha2' mkmat x local i = 1 forv i = 1/`nrep'{ gen u`i' = 0.28*invnorm(uniform()) mkmat u`i' matrix epsilon`i' = invIlambdaW*u`i' matrix y`i' = constant + x + epsilon`i' svmat y`i' local i = `i' + 1 } * * 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**:**st: RE: AW: Simulation in Stata***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**References**:**st: Simulation in Stata***From:*"Susan Olivia" <olivia@primal.ucdavis.edu>

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