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Minimum Distance Estimation of Search Costs using Price Distribution

Sanches, F, Silva Junior, D and Srisuma, Sorawoot (2017) Minimum Distance Estimation of Search Costs using Price Distribution Journal of Business & Economic Statistics, 36 (4).

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Hong and Shum (2006) show equilibrium restrictions in a search model can be used to identify quantiles of the search cost distribution from observed prices alone. These quantiles can be difficult to estimate in practice. This paper uses a minimum distance approach to estimate them that is easy to compute. A version of our estimator is a solution to a nonlinear least squares problem that can be straightforwardly programmed on softwares such as STATA. We show our estimator is consistent and has an asymptotic normal distribution. Its distribution can be consistently estimated by a bootstrap. Our estimator can be used to estimate the cost distribution nonparametrically on a larger support when prices from heterogeneous markets are available. We propose a two-step sieve estimator for that case. The first step estimates quantiles from each market. They are used in the second step as generated variables to perform nonparametric sieve estimation. We derive the uniform rate of convergence of the sieve estimator that can be used to quantify the errors incurred from interpolating data across markets. To illustrate we use online bookmaking odds for English football leagues matches (as prices) and find evidence that suggests search costs for consumers have fallen following a change in the British law that allows gambling operators to advertise more widely.

Item Type: Article
Subjects : Economics
Divisions : Faculty of Arts and Social Sciences > School of Economics
Authors :
Sanches, F
Silva Junior, D
Date : 12 June 2017
DOI : 10.1080/07350015.2016.1247003
Copyright Disclaimer : This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Business & Economic Statistics on 12 June 2017 available online:
Uncontrolled Keywords : Sieve Estimation, Search Cost, M-Estimation, Generated Variables, Bootstrap
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 28 Sep 2016 08:52
Last Modified : 05 Apr 2019 09:42

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