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From |
"Susan Olivia" <olivia@primal.ucdavis.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Simulation in Stata |

Date |
Wed, 27 May 2009 16:28:44 -0700 |

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/

**Follow-Ups**:**st: AW: Simulation in Stata***From:*"Martin Weiss" <martin.weiss1@gmx.de>

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