Standard Errors for Calibrated Parameters.
Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate of the correlation structure of the empirical moments, which...
| Publicado en: | Review of Economic Studies Vol. 92; no. 5; pp. 2952 - 2979 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=188607446&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 188607446 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00346527 REM jtl: Review of Economic Studies issn: 00346527 maglogo: N pubinfo: dt: Oct2025 vid: 92 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 188607446 10.1093/restud/rdae099 ppf: 2952 ppct: 27 formats: tig: atl: Standard Errors for Calibrated Parameters. aug: au: Cocci, Matthew D Plagborg-Møller, Mikkel affil: Amazon Web Services, USA Department of Economics, Princeton University, USA su: Calibration Parameter estimation Inferential statistics Statistical errors Estimation theory Confidence intervals Moments method (Statistics) Structural models sug: subj: Calibration Parameter estimation Inferential statistics Statistical errors Estimation theory Confidence intervals Moments method (Statistics) Structural models keyword: Data combination Minimum distance Moment selection Semidefinite programming Data combination Minimum distance Moment selection Semidefinite programming ab: Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate of the correlation structure of the empirical moments, which is often unavailable in practice. Instead, the variances of the individual empirical moments are usually readily estimable. Using only these variances, we derive conservative standard errors and confidence intervals for the structural parameters that are valid even under the worst-case correlation structure. In the over-identified case, we show that the moment weighting scheme that minimizes the worst-case estimator variance amounts to a moment selection problem with a simple solution. Finally, we develop tests of over-identifying or parameter restrictions. We apply our methods empirically to a model of menu cost pricing for multi-product firms and to a heterogeneous agent New Keynesian model. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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