Robust asymmetric information models yield stable predictions across environments
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REFUTED
the evidence says no
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Recent economic and theoretical work indicates that asymmetric information serves as a source of equilibrium indeterminacy and multiple equilibria, rather than consistently yielding stable predictions across environments.
Recent theoretical work has found asymmetric information to serve as an independent source of equilibrium indeterminacy—and thereby sunspot-driven fluctuations—in otherwise standard rational expectations models. Established in environments with fully informed private agents and an imperfectly informed policymaker, we prove this result is robust to reversing the informational hierarchy, and its implications for equilibrium dynamics enriched. Using a small-scale New Keynesian model featuring monetary policy opacity, we show that private agents’ optimal projections of the unobservables vis-à-vis a fully informed central bank generically entail (i) multiple linear equilibria notwithstanding fulfillment of the Taylor principle; and (ii) feasible equilibrium paths along which sunspot shocks affect inflation but not output dynamics, or viceversa. While identifying a role for policy transparency in preventing belief-driven macroeconomic volatility, our formal results underscore the ability of informational frictions in business cycle models to account for empirical facts that are at odds with the theory of homogeneous, full information rational expectations.
Analysis of yield gaps were conducted in the context of crop insurance and used to build an indicator of asymmetric information. The possible influence of asymmetric information in the decision of Spanish wheat producers to contract insurance was additionally evaluated. The analysis includes simulated yield using a validated crop model, CERES-Wheat previously selected among others, whose suitability to estimate actual risk when no historical data are available was assessed. Results suggest that the accuracy in setting the insured yield is decisive in farmers’ willingness to contract crop insurance under the wider coverage. Historical insurance data, when available, provide a more robust technical basis to evaluate and calibrate insurance parameters than simulated data, using crop models. Nevertheless, the use of crop models might be useful in designing new insurance packages when no historical data is available or to evaluate scenarios of expected changes. In that case, it is suggested that yield gaps be estimated and considered when using simulated attainable yields.
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