Nonparametric Estimation of the Costs of Non-Sequential Search
Jose Luis Moraga-Gonzalez
Matthijs R. Wildenbeest
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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