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Título: | Learning and Convergence to a Full-Information Equilibrium are not Equivalent |
Autor: | Jun, B.; Vives, Xavier | Fecha de publicación: | 1996 | Editor: | Oxford University Press | Citación: | Review of Economic Studies 63(4): 653-674 (1996) | Resumen: | Convergence to a full-information equilibrium (FIE) in the presence of persistent shocks and asymmetric information about an unknown payoff-relevant parameter θ is established in a classical infinite-horizon partial equilibrium linear model. It is found that, under the usual stability assumptions on the autoregressive process of shocks, convergence occurs at the rate n-1/2, where n is the number of rounds of trade, and that the asymptotic variance of the discrepancy of the full-information price and the market price is independent of the degree of autocorrelation of the shocks. This is so even though the speed of learning θ from prices becomes arbitrarily slow as autocorrelation approaches a unit root level. It follows then that learning the unknown parameter θ and convergence of the equilibrium process to the FIE are not equivalent. Moreover, allowing for non-stationary processes of shocks, the distinction takes a more stark form. Learning θ is neither necessary nor sufficient for convergence to the FIE. When the process of shocks has a unit root, convergence to the FIE occurs but θ can not be learned. When the process is sufficiently explosive and there is a positive mass of perfectly informed agents, θ is learned quickly but convergence to the FIE does not occur. | URI: | http://hdl.handle.net/10261/57907 | DOI: | 10.2307/2297798 | Identificadores: | doi: 10.2307/2297798 issn: 0034-6527 e-issn: 1467-937X |
Aparece en las colecciones: | (IAE) Artículos |
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