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From |
Steven Samuels <ssamuels@albany.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: bootstrapping standard errors with several estimated regressors + 1st line: |

Date |
Mon, 9 Jul 2007 17:09:22 -0400 |

Obstreperous line

The last post with all lines was:

Erasmo, What was the basis for your original thought "that bootstrapping would cause statistical significance for all regressors to go down"? I've not seen this in the bootstrap literature. Indeed, your example, and that of Maarten, suggest that there is no order relation between model-based estimated standard errors and those estimated by the bootstrap.

You might be thinking that bootstrapping should cause p-values to rise because regressors, as well as responses, are being sampled. This is not so. Assume the classical multiple regression model. If the X variables are random and independent of independent of the error terms, then in the usual formula for the V(b), (X'X)^(-1) is replaced by its expectation. (WH Greene, Econometrics, McMillan, 1990).

You might also be thinking that the use of estimated regressors should lead to higher higher pvalues, compared to having the "true" regressors. This sounds right, although I am not expert in this area, but it is irrelevant. Both original and bootstrapped standard errors are based on the estimated regressors.

Perhaps you are confusing the estimates of coefficients with estimates of standard errors of coefficients. If model assumptions are right, then both model-based estimate of standard error and the bootstrap estimates of standard error are "good" estimates of the same quantity, the "true" standard error. However, the model-based estimate benefits from knowing that model is true. For example, in OLS, for example, the key assumption is that there is a constant SD. The model-based estimate standard error is therefore a function of one quantity besides the X'X matrix, namely the residual SD. The bootstrap estimate is valid even if the residual SD is not constant, as long as the observations are uncorrelated. The price for this greater validity is that, if the model is right, the bootstrap estimate of standard error will be more variable then the model-based estimate. See Efron & Tibshirani, An Introduction to the Bootstrap, Chapman & Hall, 1994.

-Steve

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**Follow-Ups**:**Re: st: RE: bootstrapping standard errors with several estimated regressors + 1st line:***From:*"Erasmo Giambona" <e.giambona@gmail.com>

**References**:**st: RE: bootstrapping standard errors with several estimated regressors***From:*"Maarten Buis" <M.Buis@fsw.vu.nl>

**Re: st: RE: bootstrapping standard errors with several estimated regressors***From:*"Erasmo Giambona" <e.giambona@gmail.com>

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