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Dynamic selection and distributional bounds on search costs in dynamic unit‐demand models

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  • Jason R. Blevins
  • Garrett T. Senney
Abstract
This paper develops a dynamic model of consumer search that, despite placing very little structure on the dynamic problem faced by consumers, allows us to exploit intertemporal variation in price distributions to estimate the distribution from which consumer search costs are initially drawn. We show that static approaches to estimating this distribution may suffer from dynamic sample selection bias. This can happen if consumers are forward‐looking and delay their purchases in a way that systematically depends on their individual search costs. We consider identification of the population search cost distribution using only price data and develop estimable nonparametric upper and lower bounds on the distribution function, as well as a nonlinear least squares estimator for parametric models. We also consider the additional identifying power of weak, theoretical assumptions such as monotonicity of purchase probabilities in search costs. We apply our estimators to analyze the online market for two widely used econometrics textbooks. Our results suggest that static estimates of the search cost distribution are biased upwards, in a distributional sense, relative to the true population distribution. We illustrate this and other forms of bias in a small‐scale simulation study.

Suggested Citation

  • Jason R. Blevins & Garrett T. Senney, 2019. "Dynamic selection and distributional bounds on search costs in dynamic unit‐demand models," Quantitative Economics, Econometric Society, vol. 10(3), pages 891-929, July.
  • Handle: RePEc:wly:quante:v:10:y:2019:i:3:p:891-929
    DOI: 10.3982/QE728
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    References listed on IDEAS

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    1. Fabio Sanches & Daniel Silva Junior & Sorawoot Srisuma, 2018. "Minimum Distance Estimation of Search Costs Using Price Distribution," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 658-671, October.

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    More about this item

    JEL classification:

    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection

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