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Bayesian estimation of nonlinear equilibrium models with random coefficients

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  • V. Brian Viard
  • Anne Gron
  • Nicholas G. Polson

Abstract

In this paper, we develop a conditional likelihood based approach for estimating the equilibrium price and shares in markets with differentiated products and oligopoly supply. We model market demand using a discrete choice model with random coefficients and random utility. For most applications, the likelihood function of equilibrium prices and shares is intractable and cannot be directly analyzed. To overcome this, we develop a Markov Chain Monte Carlo simulation strategy to estimate parameters and distributions. To illustrate our methodology, we generate a dataset of prices and quantities simulated from a differentiated goods oligopoly across a number of markets. We apply our methodology to this dataset to demonstrate its attractive features as well as its accuracy and validity. Copyright © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • V. Brian Viard & Anne Gron & Nicholas G. Polson, 2015. "Bayesian estimation of nonlinear equilibrium models with random coefficients," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(4), pages 435-456, July.
    • Handle: RePEc:wly:apsmbi:v:31:y:2015:i:4:p:435-456
    DOI: 10.1002/asmb.2036
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