Production risk and the estimation of ex-ante cost functions.
Cost function estimation under production uncertainty is problematic because the relevant cost is conditional on unobservable expected output. If input demand functions are also stochastic, then a nonlinear errors-in-variables model is obtained and standard estimation procedures typically fail to at...
| Publicado en: | Journal of Econometrics Vol. 100; no. 2; pp. 357 - 381 |
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| Formato: | Artículo |
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Elsevier Science
February 2001
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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=513068148&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513068148 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03044076 ECM jtl: Journal of Econometrics issn: 03044076 maglogo: N pubinfo: dt: February 2001 vid: 100 iid: 2 pid: 1004 pub: Elsevier Science artinfo: ui: 513068148 10.1016/S0304-4076(00)00041-5 ppf: 357 ppct: 24 formats: tig: atl: Production risk and the estimation of ex-ante cost functions. aug: au: Moschini, GianCarlo su: Mathematical models in business Industrial costs Estimation theory Risk Uncertainty Economics sug: subj: Mathematical models in business Industrial costs Estimation theory Risk Uncertainty Economics ab: Cost function estimation under production uncertainty is problematic because the relevant cost is conditional on unobservable expected output. If input demand functions are also stochastic, then a nonlinear errors-in-variables model is obtained and standard estimation procedures typically fail to attain consistency. But by exploiting the full implications of the expected profit maximization hypothesis that gives rise to ex-ante cost functions, it is shown that the errors-in-variables problem can be effectively removed, and consistent estimation of the parameters of interest achieved. A Monte Carlo experiment illustrates the advantages of the proposed procedure as well as the pitfalls of other existing estimators. Copyright (c) 2000 Elsevier Science S.A. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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