Semi-Nonparametric Estimation of Consumer Search Costs
AbstractThis 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.
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Bibliographic InfoPaper provided by Indiana University, Kelley School of Business, Department of Business Economics and Public Policy in its series Working Papers with number 2007-20.
Date of creation: Dec 2007
Date of revision: Jun 2010
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consumer search; oligopoly; search costs; semi-nonparametric estimation;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- D43 - Microeconomics - - Market Structure and Pricing - - - Oligopoly and Other Forms of Market Imperfection
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-01-12 (All new papers)
- NEP-ECM-2008-01-12 (Econometrics)
- NEP-MIC-2008-01-12 (Microeconomics)
- NEP-MKT-2008-01-12 (Marketing)
- NEP-ORE-2008-01-12 (Operations Research)
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