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From |
"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu> |

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

Subject |
RE: Re: st: RE: RE: Bootstrap and Technical analysis |

Date |
Wed, 20 Aug 2008 11:45:01 -0700 |

I had a conversation about which interval to use with David Banks several years ago and he noted that either the percentile interval or the bias-corrected percentile interval was preferable to the normal interval which seems to make more assumptions. Tony Peter A. Lachenbruch Department of Public Health Oregon State University Corvallis, OR 97330 Phone: 541-737-3832 FAX: 541-737-4001 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Eva Poen Sent: Tuesday, August 19, 2008 9:14 AM To: Statalist Subject: Re: Re: st: RE: RE: Bootstrap and Technical analysis Mahmoud, I haven't followed all of the conversation. Just a few notes: 2008/8/19 Mahmoud Abd-El-Aal <ma7205@bristol.ac.uk>: > bootstrap (location: mean=r(mean)), rep(1000): sum var2,detail > then i want to save all the bootstrap samples, the 1000 samples in order > to compare each individual mean to var1 single mean Firstly, you don't need the detail option in your bootstrap command. You can save the bootstrap results by using the -saving- option, see -help bootstrap-. That would be set seed 123 bootstrap (location: mean=r(mean)), rep(1000) saving(bootstrapsample, replace): sum var2 You could then open the file bootstrapsample.dta and investigate the 1000 replications. > Also when running the above command , the P value what does it represent? It might help you to read the manual entry on -bootstrap-, and/or some statistics text on the bootstrap technique. This will tell you about the different confidence intervals that you can calculate after -bootstrap- (normal based, percentile based, bias corrected). Type -estat bootstrap, all- after running the bootstrap to see all confidence intervals. The P value refers to a test that the original sample mean is equal to zero, very much like a coefficient test after -regress-. It uses the estimated standard error from the bootstrap instead of the sample standard error. A low p-value rejects the hypothesis that the original sample mean is equal to zero. However, this is based on the normal approximation. You might find one of the other methods more appropriate in your circumstances. > The basic point that i am trying to reach is how many samples from the > bootstraped ones has a bigger mean than the value of 0.0001218, any > suggestions? I showed you how to do this (save the bootstrap replications in a .dta file), but I am unsure how you would sensibly interpret such a finding. Hope this helps, Eva * * 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/

**References**:**RE: Re: st: RE: RE: Bootstrap and Technical analysis***From:*Mahmoud Abd-El-Aal <ma7205@bristol.ac.uk>

**Re: Re: st: RE: RE: Bootstrap and Technical analysis***From:*"Eva Poen" <eva.poen@gmail.com>

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