Abstract
When Muth [1961] introduced the rational-expectations hypothesis (REH), his basic idea was that agents form expectations by rationally acquiring and processing information (weak REH). From this, he immediately jumped to a stronger hypothesis (strong REH), which in the following years radically transformed macroeconomic theory and policy. The strong REH is implied by the assumption that agents know the true (statistical) model of their environment. Except for trivial cases, this model comprises a causal model relating endogenous to exogenous variables, and objective probability distributions of the exogenous variables. Agents’ expectations are the objective probability distributions of future developments conditional on their current information about past realizations of exogenous and endogenous variables. In terms of the famous Knightian distinction, the strong REH implies risk and not uncertainty. Rationality then requires that agents choose a strategy (i.e., a plan specifying actions for all contingencies) that maximizes the expected utility on the basis of a v. Neumann-Morgenstern (NM) utility function.1
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Albert, M. (2001). Bayesian Learning and Expectations Formation: Anything Goes. In: Corfield, D., Williamson, J. (eds) Foundations of Bayesianism. Applied Logic Series, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1586-7_14
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