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We show that the equilibrium policy rule in beauty contest models is equivalent to that of a single agent's forecast of the economic fundamental. This forecast is conditional on a modified information process, which simply discounts the precision of idiosyncratic shocks by the degree of strategic complementarity. The result holds for any linear Gaussian signal process (static or persistent, stationary or nonstationary, exogenous or endogenous), and also extends to network games. Theoretically, this result provides a sharp characterization of the equilibrium and its properties under dynamic information. Practically, it provides a straightforward method to solve models with complicated information structures. (JEL C72, D82, D83, D84)

We develop an equivalence between the equilibrium effects of incomplete information and those of two behavioral distortions: myopia, or extra discounting of the future; and anchoring of current behavior to past behavior, as in models with habit persistence or adjustment costs. We show how these distortions depend on higher-order beliefs and GE mechanisms, and how they can be disciplined by evidence on expectations. We finally illustrate the use of our toolbox with a quantitative application in the context of inflation, a bridge to the HANK literature, and an extension to networks. (JEL C53, D83, D85, E12, E31, E37)

We study financial shocks to households' ability to borrow in an economy that quantitatively replicates U.S.earnings, financial, and housing wealth distributions and the main macro aggregates. Such shocks generate large recessions via the negative wealth effect associated with the large drop in house prices triggered by the reduced access to credit of a large number of households. The model incorporates additional margins that are crucial for a large recession to occur: that it is difficult to reallocate production from consumption to investment or net exports, and that the reductions in consumption contribute to reductions in measured TFP. ; The ADEMU Working Paper Series is being supported by the European Commission Horizon 2020 European Union funding for Research & Innovation, grant agreement No 649396.