We present a statistical test for the hypothesis of (approximate) utility maximization on the basis of nonparametric revealed preference conditions. We take as null hypothesis that the consumer behaves randomly, and we reject this hypothesis only if the data provides sufficient evidence to support the alternative hypothesis of approximate utility maximization. Our statistical test uses a permutation method to operationalize the principle of random consumption behavior. We show that our test (i) is valid for any sample size under the null and (ii) has an asymptotic power of one. We also provide simulated power results and two empirical applications.

We run an experiment to elicit preferences over state-contingent timed payouts. We analyze the data using a new revealed preference method (building on Nishimura et al., 2017) that can test for consistency with utility functions that increase with a given preorder. We find that correlation neutrality, a property implied by discounted expected utility, is widely violated and there is, instead, strong evidence of intertemporal correlation averse behavior. Our results suggest that utility is not additive across both states and time and that credible models of choice need to allow people to prefer negative correlation in timed payouts.