The authors work with a finite data set where each observation consists of a bundle of contingent consumption chosen by an agent from a constraint set of such bundles. They develop a general procedure for testing the consistency of this data set with a broad class of models of choice under risk and under uncertainty.
In this paper the authors show that theory-consistent demand analysis remains feasible in the presence of partially observed prices, and hence partially observed implied budget sets, even if we are agnostic about the nature of the missing prices.
This paper derives necessary and sufficient conditions for data sets composed of state-contingent prices and consumption to be consistent with two prominent models of decision making under uncertainty: variational preferences and smooth ambiguity.
We show that an agent maximizing some utility function on a discrete (as opposed to continuous) consumption space will obey the generalized axiom of revealed preference (GARP) so long as the agent obeys cost efficiency.