Identifying Ambiguity Shocks in Business Cycle Models Using Survey Data

Abstract

We develop a framework to analyze economies with agents facing time-varying concerns for model misspecification.These concerns lead agents to interpret economic outcomes and make decisions through the lens of a pessimistically biased 'worst-case' model.We combine survey data and implied theoretical restrictions on the relative magnitudes and comovement of forecast biases across macroeconomic variables to identify ambiguity shocks as exogenous fluctuations in the worst-case model.Our solution method delivers tractable linear approximations that preserve the effects of time-varying ambiguity concerns and permit estimation using standard Bayesian techniques.Applying our framework to an estimated New-Keynesian business cycle model with frictional labor markets, we find that ambiguity shocks explain a substantial portion of the variation in labor market quantities.

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