Semi-Nonparametric Estimation of Consumer Search Costs

Jose Moraga-Gonzalez, Zsolt Sandor and Matthijs 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 estimation of the cost of non-sequential search. We provide a new method based on semi-nonparametric (SNP) estimation that allows us to pool price data from different consumer markets with the same underlying search cost distribution but di erent valuations or selling costs. We show that pooling data from di erent markets increases the number of estimated critical search cost cuto s at all quantiles of the search cost distribution, which increases the precision of the estimates. A Monte Carlo study shows that the method works well in small samples. We apply our method to a data set of online prices for memory chips and nd that the search cost density is essentially bimodal such that a large fraction of consumers searches very little, while a smaller fraction of consumers samples a relatively large number of stores.

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)
Date: 2007-12, Revised 2010-06
New Economics Papers: this item is included in nep-ecm, nep-mic, nep-mkt and nep-ore
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Citations: View citations in EconPapers (1)

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http://kelley.iu.edu/riharbau/RePEc/iuk/wpaper/bep ... ndor-wildenbeest.pdf (application/pdf)

Related works:
Journal Article: SEMI‐NONPARAMETRIC ESTIMATION OF CONSUMER SEARCH COSTS (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:iuk:wpaper:2007-20

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