Semiparametric estimation of the binary choice model for contingent valuation.
Research was conducted to attempt to estimate the binary choice model without imposing any parametric structure on the distribution of the stochastic term. The distribution-free maximum likelihood method developed by Stephen Cosslett (1983) is adapted for deriving the contingent value function with...
| Publicado en: | Land Economics Vol. 72; pp. 462 - 474 |
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| Formato: | Artículo |
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University of Wisconsin Press
November 1996
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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=510526715&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 510526715 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00237639 LAE jtl: Land Economics issn: 00237639 maglogo: N pubinfo: dt: November 1996 vid: 72 pid: 249 pub: University of Wisconsin Press artinfo: ui: 510526715 10.2307/3146909 ppf: 462 ppct: 12 formats: fmt: @attributes: type: T tig: atl: Semiparametric estimation of the binary choice model for contingent valuation. aug: au: Li, Chuan-Zhong su: Contingent valuation Parameter estimation Monte Carlo method Natural resources Valuation Outdoor recreation Economics Forests & forestry Sweden sug: subj: Sweden Contingent valuation Parameter estimation Monte Carlo method Natural resources Valuation Outdoor recreation Economics Forests & forestry ab: Research was conducted to attempt to estimate the binary choice model without imposing any parametric structure on the distribution of the stochastic term. The distribution-free maximum likelihood method developed by Stephen Cosslett (1983) is adapted for deriving the contingent value function with respect to observable exogenous variables. Monte Carlo comparisons with the probit estimates are presented, and the asymptotic consistency and relative efficiency of the approach are discussed. Data from a forest environment valuation survey are employed for empirical estimations. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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