A Minimax Regret Approach to Decision Making Under Uncertainty

Ashok K. Mishra, Mike G. Tsionas

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We propose a minimax regret approach to optimal factor demand under uncertainty. Regret is the deviation of any given decision from the optimal decision based on a specified set of possible scenarios for the uncertain variables. This approach does not require the specification of instrumental variables to control for unobserved states of nature, and also does not require specification of the number of possible states in advance. Importantly, ex post production shocks can be estimated using our approach, and full statistical inferences can be obtained. Econometric techniques are based on Bayesian analysis using Markov Chain Monte Carlo techniques. A substantive empirical application is provided to illustrate the new approach.

Original languageEnglish (US)
JournalJournal of Agricultural Economics
StateAccepted/In press - Jan 1 2020


  • Bayesian analysis
  • Markov Chain Monte Carlo
  • minimax regret
  • production decisions
  • uncertainty

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Economics and Econometrics


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