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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Bias: Monte Carlo |

Date |
Mon, 06 May 2013 22:32:18 +0200 |

Thanks Stas, Maarten. Best, J. __________________________________________ John Antonakis Professor of Organizational Behavior Director, Ph.D. Program in Management 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 http://www.hec.unil.ch/people/jantonakis Associate Editor The Leadership Quarterly __________________________________________ On 06.05.2013 17:04, Stas Kolenikov wrote:

Continuing on Maarten's note, my -bsweights- package produces first-order balanced bootstrap weights that remove the random simulation error from the bootstrap results for the point estimates. See http://stata-journal.com/article.html?article=st0187 and references on the balanced bootstrap therein. I think the bias in parameter estimates should be put into the context of MSE: if the bias component is greater than the variance component, then the estimator is relatively useless. If the magnitude of bias is say 1/2 that of the standard deviation of the sampling distribution of your estimator, so that the contribution of bias to the total MSE is 20%, I would personally be able to live with that. With bias this big, though, your estimation procedure should report the standard errors that are based on MSE, not on the variance alone. For lots of "regularly behaving" estimators, the sampling standard deviation (estimated by the standard error) is O(n^{-1/2}), and bias is O(n^{-1}), going to zero faster with the sample size than the standard deviation, so in large samples, the bias asymptotically disappears. Then the question of "how large bias is tolerable" becomes the question of "how large my sample size should be" (for the normal approximations to make sense). Finally, the % bias is a awful measure when the true value of the parameter is zero (and the whole situation is shift invariant, as in the mean of the normal distribution, so the initial point on the scale is arbitrary). It's probably OK in the constant CV situation of heavily skewed distributions, but may not make sense in other situations. -- Stas Kolenikov, PhD, PStat (SSC) -- Senior Survey Statistician, Abt SRBI -- Opinions stated in this email are mine only, and do not reflect the position of my employer -- http://stas.kolenikov.name On Mon, May 6, 2013 at 3:25 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:On Mon, May 6, 2013 at 9:49 AM, John Antonakis wrote:I am running some Monte Carlos where I am interested in observing the bias in parameter estimates across manipulated conditions. By bias I mean the absolute percentage difference of the simulated value from the true value. I was wondering whether there has been another written about how much bias is "acceptable"--I know that this is like asking how long is a piece of string and that there is no statistical fiat that can give a definitive answer, because it also is a very field specific issue.It is probably not quite the answer you are looking for (and I think you are right by wondering whether such an answer can exist), but one thing you can do is take into account that a Monte Carlo experiment contains a random component, so if you repeat the experiment (with a different seed) you will get a slightly different estimate of your bias. The logic behind this variation between Monte Carlo experiments is pretty much the same as the logic behind statistical testing: so you can compute standard errors and confidence intervals. This is the idea behind: Ian R. White (2010) "simsum: Analyses of simulation studies including Monte Carlo error" The Stata Journal, 10(3):369--385 and <http://www.maartenbuis.nl/software/simpplot.html>. It is not very useful as a definition of what amount of bias is "acceptable" as you can arbitrarily make the bounds around your estimate of the bias smaller by increasing the number of iterations, but at least this type of bounds prevents you from over-interpretting the result from your simulation, as happend here: <http://stats.stackexchange.com/questions/55676#55676>. Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

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**References**:**st: Bias: Monte Carlo***From:*John Antonakis <John.Antonakis@unil.ch>

**Re: st: Bias: Monte Carlo***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Bias: Monte Carlo***From:*Stas Kolenikov <skolenik@gmail.com>

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