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Existence of an equilibrium for infinite horizon economies with and without complete information

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Abstract

This work proves the existence of an equilibrium for an infinite horizon economy where trade takes place sequentially over time. There exist two types of agents: the first correctly anticipates all future contingent endogenous variables with complete information as in Radner [Radner, R. (1972). Existence of equilibrium of plans, prices and price expectations in a sequence of markets. Econometrica, 289–303] and the second has exogenous expectations about the future environment as in Grandmont [Grandmont, J. M. (1977). Temporary general equilibrium theory. Econometrica, 535–572] and information based on the current and past aggregate variables including those which are private knowledge. Agents with exogenous expectations may have inconsistent optimal plans but have predictive beliefs in the context of Blackwell and Dubbins [Blackwell, D., Dubins, L. (1962). Merging of opinions with increasing information. The Annals of Mathematical Statistics, 882–886] with probability transition rules based on all observed variables. We provide examples of this framework applied to models of differential information and environments exhibiting results of market selection and convergence of an equilibrium. The existence result can be used to conclude that, by adding the continuity assumption on the probability transition rules, we obtain the existence of an equilibrium for some models of differential information and incomplete markets.

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  • Raad, R., 2012. "Existence of an equilibrium for infinite horizon economies with and without complete information," Journal of Mathematical Economics, Elsevier, vol. 48(4), pages 247-262.
  • Handle: RePEc:eee:mateco:v:48:y:2012:i:4:p:247-262
    DOI: 10.1016/j.jmateco.2012.06.001
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    1. Rodrigo Raad, 2016. "Recursive equilibrium with Price Perfect Foresight and a minimal state space," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 61(1), pages 1-54, January.

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