The principal-agent approach to testing experts

Recent literature on testing experts shows that it is difficult, and often impossible, to determine whether an expert knows the stochastic process that generates data. Despite this negative result, we show that often exist contracts that allow a decision maker to attain the first-best payoff in the following sense: in the case in which the expert knows the stochastic process, the decision maker achieves the payoff she would obtain if there were no incentive problems; while in the case in which the expert does not know the stochastic process, she achieves the payoff she would obtain in the absence of any expert. More precisely, this kind of full-surplus extraction is always possible in infinite-horizon models in which future payoffs are not discounted. If future payoffs are discounted (but the discount factor tends to 1), the possibility of full-surplus extraction depends on a constraint involving the forecasting technology.