Bootstrap methods for covariance structures.
The optimal minimum distance (OMD) estimator for models of covariance structures is asymptotically efficient but has much worse finite-sample properties than does the equally weighted minimum distance (EWMD) estimator. This paper shows how the bootstrap can be used to improve the finite-sample perf...
| Published in: | Journal of Human Resources Vol. 33; no. 1; pp. 39 - 62 |
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| Format: | Article |
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University of Wisconsin Press
Winter98
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=507618847&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 507618847 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0022166X JHR jtl: Journal of Human Resources issn: 0022166X maglogo: N pubinfo: dt: Winter98 vid: 33 iid: 1 pid: 249 pub: University of Wisconsin Press artinfo: ui: 507618847 10.2307/146314 ppf: 39 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.2MB tig: atl: Bootstrap methods for covariance structures. aug: au: Horowitz, Joel L. su: Statistical bootstrapping Estimation theory Analysis of covariance Confidence intervals Mathematical models Welfare economics Mathematical models of income sug: subj: Statistical bootstrapping Estimation theory Analysis of covariance Confidence intervals Mathematical models Welfare economics Mathematical models of income ab: The optimal minimum distance (OMD) estimator for models of covariance structures is asymptotically efficient but has much worse finite-sample properties than does the equally weighted minimum distance (EWMD) estimator. This paper shows how the bootstrap can be used to improve the finite-sample performance of the OMD estimator. The theory underlying the bootstrap's ability to reduce the bias of estimators and errors in the coverage probabilities of confidence intervals is summarized. The results of numerical experiments and an empirical example show that the bootstrap often essentially eliminates the bias of the OMD estimator. The finite-sample estimation efficiency of the bias-corrected OMD estimator often exceeds that of the EWMD estimator. Moreover, the true coverage probabilities of confidence intervals based on the OMD estimator with bootstrap-critical values are very close to the nominal coverage probabilities. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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