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A Poisson mixture model of discrete choice

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  • Burda, Martin
  • Harding, Matthew
  • Hausman, Jerry

Abstract

In this paper, we introduce a new Poisson mixture model for count panel data where the underlying Poisson process intensity is determined endogenously by consumer latent utility maximization over a set of choice alternatives. This formulation accommodates the choice and count in a single random utility framework with desirable theoretical properties. Individual heterogeneity is introduced through a random coefficient scheme with a flexible semiparametric distribution. We deal with the analytical intractability of the resulting mixture by recasting the model as an embedding of infinite sequences of scaled moments of the mixing distribution, and newly derive their cumulant representations along with bounds on their rate of numerical convergence. We further develop an efficient recursive algorithm for fast evaluation of the model likelihood within a Bayesian Gibbs sampling scheme. We apply our model to a recent household panel of supermarket visit counts. We estimate the nonparametric density of three key variables of interest–price, driving distance, and their interaction–while controlling for a range of consumer demographic characteristics. We use this econometric framework to assess the opportunity cost of time and analyze the interaction between store choice, trip frequency, search intensity, and household and store characteristics. We also conduct a counterfactual welfare experiment and compute the compensating variation for a 10%–30% increase in Walmart prices.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 166 (2012)
Issue (Month): 2 ()
Pages: 184-203

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Handle: RePEc:eee:econom:v:166:y:2012:i:2:p:184-203

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Bayesian nonparametric analysis; Markov chain Monte Carlo; Dirichlet process prior;

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References

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  1. Patrick Bajari & Jeremy Fox & Kyoo il Kim & Stephen P. Ryan, 2009. "The Random Coefficients Logit Model Is Identified," NBER Working Papers 14934, National Bureau of Economic Research, Inc.
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  13. Christian Broda & Ephraim Leibtag & David E. Weinstein, 2009. "The Role of Prices in Measuring the Poor's Living Standards," Journal of Economic Perspectives, American Economic Association, vol. 23(2), pages 77-97, Spring.
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  15. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2008. "A Bayesian mixed logit-probit model for multinomial choice," Journal of Econometrics, Elsevier, vol. 147(2), pages 232-246, December.
  16. Terza, Joseph V., 1998. "Estimating count data models with endogenous switching: Sample selection and endogenous treatment effects," Journal of Econometrics, Elsevier, vol. 84(1), pages 129-154, May.
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