We introduce tests for finite-sample linear regressions with heteroskedastic errors. The tests are exact, i.e., they have guaranteed type I error probabilities when bounds are known on the range of the dependent variable, without any assumptions about the noise structure. We provide upper bounds...

We consider an agent who has to repeatedly make choices in an uncertain and changing environment, who has full information of the past, who discounts future payoffs, but who has no prior. We provide a learning algorithm that performs almost as well as the best of a given finite number of experts...

Small sample properties are of fundamental interest when only limited data is available. Exact inference is limited by constraints imposed by specific nonrandomized tests and of course also by lack of more data. These effects can be separated as we propose to evaluate a test by comparing its...

We introduce a solution concept for extensive-form games of incomplete information in which players are allowed to entertain multiple beliefs about what they do not know in the game. Players make dynamically consistent choices by looking for compromises that yield a good performance under each...

Individuals belonging to two large populations are repeatedly randomly matched to play a cyclic "times" game such as Matching Pennies. Between matching rounds, individuals sometimes change their strategy after observing a finite sample of other outcomes within their population. Individuals from...

We present methods of belief elicitation which are applicable for any non-trivial utility function. Unlike existing techniques that account for deviations from risk-neutrality, these methods are highly transparent to subjects. Rather than identifying beliefs exactly we identify bounds on...