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Testing for Optimization Behavior in Production when Data is with Measurement Errors: A Bayesian Approach

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Abstract
The purpose of this paper is to develop formal tests for cost / profitt rationalization of observed data sets under measurement errors in both prices and quantities. The new techniques are based on new statistical formulations for inequalities that describe cost and pro t rationalizability, developed in a Bayesian framework. The new likelihood-based methods of inference are introduced and then illustrated using a data set of large U.S. banks. We also develop various robustness checks, including a normal and lognormal speci cation of the data generating process, as well as a multivariate mixture-of-normal-distributions.

Suggested Citation

  • Mike G. Tsionas & Valentin Zelenyuk, 2022. "Testing for Optimization Behavior in Production when Data is with Measurement Errors: A Bayesian Approach," CEPA Working Papers Series WP012022, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:173
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    File URL: https://economics.uq.edu.au/files/33923/WP012022.pdf
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    References listed on IDEAS

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    Keywords

    Cost Minimization; Profit Maximization; Likelihood-based methods; Markov Chain Monte Carlo; Banking.;
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