Robust decision making for cost minimization under uncertainty

Relatore
Mike Tsionas - University of Lancaster

Abstract
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. The new approach does not require the specification of instrumental variables to back out unobserved states of nature, neither it requires specifying the number of possible states in advance. Importantly, ex post production shocks can be estimated using the new approach, and full statistical inferences can be performed. Econometric techniques are based on Bayesian analysis using Markov Chain Monte Carlo techniques. A substantive empirical application is provided to illustrate the new techniques.

Organizzazione
Prof. Giorgio Tassinari