Nonparametric Estimation of the Costs of Non-Sequential Search

Jose Luis Moraga-Gonzalez (), Zsolt Sandor and Matthijs R. Wildenbeest ()
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Zsolt Sandor: Universidad Carlos III de Madrid

No 2007-20, Working Papers from Indiana University, Kelley School of Business, Department of Business Economics and Public Policy

Abstract: This paper studies the nonparametric identification and estimation of the costs of non-sequential search. Since the sequence of points of the search cost distribution that are identifiable is convergent to zero, using data from just one market search costs can only be identified accurately at low quantiles. To solve this problem, we propose to consider a richer framework, where the researcher has price data from many markets with the same underlying search cost distribution, and provide identification conditions in such setting. To exploit the fact that the same search technology prevails in all the markets, we propose a new estimator based on semi-nonparametric density estimation. A Monte Carlo study illustrates the new approach and an application using a data set of online prices for memory chips is presented.

Keywords: consumer search; oligopoly; search costs; semi-nonparametric estimation (search for similar items in EconPapers)
JEL-codes: C14 D43 D83 L13 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-mic, nep-mkt and nep-ore
Date: 2007-12

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http://www.bus.indiana.edu/riharbau/RePEc/iuk/wpap ... ndor-wildenbeest.pdf (application/pdf)

Related works:
Working Paper: Nonparametric Estimation of the Costs of Non-Sequential Search (2008)
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Persistent link: http://EconPapers.repec.org/RePEc:iuk:wpaper:2007-20

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