A computationally fast estimator for random coefficients logit demand models using aggregate data.
This article proposes a computationally fast estimator for random coefficients logit demand models using aggregate data that Berry, Levinsohn, and Pakes (; hereinafter, BLP) suggest. Our method, which we call approximate BLP (ABLP), is based on a linear approximation of market share functions. The c...
| Publicado en: | RAND Journal of Economics (Wiley-Blackwell) Vol. 46; no. 1; pp. 86 - 103 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
Spring2015
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| Materias: | |
| 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=100670177&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 100670177 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07416261 56RJ jtl: RAND Journal of Economics (Wiley-Blackwell) issn: 07416261 maglogo: Y pubinfo: dt: Spring2015 vid: 46 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 100670177 10.1111/1756-2171.12078 ppf: 86 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P size: 174KB tig: atl: A computationally fast estimator for random coefficients logit demand models using aggregate data. aug: au: Lee, Jinhyuk Seo, Kyoungwon affil: Ulsan National Institute of Science and Technology Korea Advanced Institute of Science and Technology su: Logits Biomathematics Logarithms Approximation theory Functional analysis sug: subj: Logits Biomathematics Logarithms Approximation theory Functional analysis ab: This article proposes a computationally fast estimator for random coefficients logit demand models using aggregate data that Berry, Levinsohn, and Pakes (; hereinafter, BLP) suggest. Our method, which we call approximate BLP (ABLP), is based on a linear approximation of market share functions. The computational advantages of ABLP include (i) the linear approximation enables us to adopt an analytic inversion of the market share equations instead of a numerical inversion that BLP propose, (ii) ABLP solves the market share equations only at the optimum, and (iii) it minimizes over a typically small dimensional parameter space. We show that the ABLP estimator is equivalent to the BLP estimator in large data sets. Our Monte Carlo experiments illustrate that ABLP is faster than other approaches, especially for large data sets. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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