Jose Luis Moraga-Gonzalez (University of Groningen and CESifo) Zsolt Sandor (Universidad Carlos III de Madrid) Matthijs R. Wildenbeest (Department of Business Economics and Public Policy, Indiana University Kelley School of Business)
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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.
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Paper provided by Indiana University, Kelley School of Business, Department of Business Economics and Public Policy in its series Working Papers with number
2007-20.
Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods 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
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Varian, Hal R, 1980.
"A Model of Sales,"
American Economic Review,
American Economic Association, vol. 70(4), pages 651-59, September.
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