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From |
"Doogar, Rajib" <[email protected]> |

To |
"'[email protected]'" <[email protected]> |

Subject |
st: Bootstrap estimate of variance |

Date |
Fri, 17 Aug 2007 11:30:58 -0500 |

I would appreciate any help with the following issue/questions. Objective: Given n (iid ~ N(0,s)) observations on x, I want to test x_i=0, (i, 1..n) (one test for each observation). s is unknown. The n observations come from a "perturbed" environment, raising concerns that the in-sample estimate of variance may not be the correct estimate to use in the test. Fortunately I also have m observations from a "unperturbed" or "normal" environment available. Observation: I could always use the variance of the m observations to estimate s. Questions: 1. Can I do better by using bootstrap, e.g.: // estimate variance based on random sample, store estimates in bsvar.dta bs var=r(Var), reps(R) [size(Z)] [seed(S)] [nodots] saving(bsvar, replace): summ var1 // obtain mean of R estimated variances use bsvar.dta, clear summ var 2. In particular, would the mean of "var" reported by the final "summ" command in the sequence above be a legitimate bootstrap estimate of the variance of x during the "normal" periods? Many thanks, Rajib Doogar, Department of Accountancy, The University of Illinois at Urbana-Champaign 1206 S. Sixth Street, Champaign, IL 61820 Ph: 217.244.8083, Fax: 217.244.0902 http://www.cba.uiuc.edu/doogar * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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