Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems.
We provide an asymptotic distribution theory for a class of generalized method of moments estimators that arise in the study of differentiated product markets when the number of observations is associated with the number of products within a given market. We allow for three sources of error: samplin...
| Publicado en: | Review of Economic Studies Vol. 71; no. 3; pp. 613 - 655 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / UK
July 2004
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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=513179436&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513179436 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: July 2004 vid: 71 iid: 3 pid: 622 pub: Oxford University Press / UK artinfo: ui: 513179436 10.1111/j.1467-937X.2004.00298.x ppf: 613 ppct: 42 formats: tig: atl: Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems. aug: au: Berry, Steve Linton, Oliver B. Pakes, Ariel su: Parameter estimation Mathematical models of marketing Product differentiation Limit theorems Economic demand Mathematical models sug: subj: Parameter estimation Mathematical models of marketing Product differentiation Limit theorems Economic demand Mathematical models ab: We provide an asymptotic distribution theory for a class of generalized method of moments estimators that arise in the study of differentiated product markets when the number of observations is associated with the number of products within a given market. We allow for three sources of error: sampling error in estimating market shares, simulation error in approximating the shares predicted by the model, and the underlying model error. It is shown that the estimators are CAN provided the size of the consumer sample and the number of simulation draws grow at a large enough rate relative to the number of products. We consider the implications of the results for the Berry, Levinsohn and Pakes (1995) random coefficient logit model and the pure characteristics model analysed in Berry and Pakes (2002). The required rates differ for these two frequently used demand models. A small Monte Carlo study shows that the differences in asymptotic properties of the two models are reflected, in quite a striking way, in the models' small sample properties. Moreover the limit distributions provide a good approximation to the actual Monte Carlo distribution of the parameter estimates. The results have important implications for the computational burden of the two models. 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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