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Risky Linear Approximations

Alexander Meyer-Gohde

No SFB649DP2014-034, SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

Abstract: I construct risk-corrected approximations of the policy functions of DSGEmodels around the stochastic steady state and ergodic mean that are linear in the state variables. The resulting approximations are uniformly more accurate than standard linear approximations and capture the dynamics of asset pricing variables such as the expected risk premium missed by standard linear approximations. The algorithm is fast and reliable, requiring only the solution of linear equations using standard perturbation output. I examine the joint macroeconomic and asset pricing implications of a real business cycle model with stochastic trends and recursive preferences. The method is able to estimate risk aversion under these preferences using the Kalman filter, where a standard linear approximation provides no information and alternative methods require computationally intensive particle filters subject to sampling variation.

Keywords: DSGE; Solution methods; Ergodic mean; Stochastic steady state; Perturbation (search for similar items in EconPapers)
JEL-codes: C61 C63 E17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-mac, nep-ore and nep-upt
Date: 2014-07
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Software Item: Dynare add-on for "Risk-Sensitive Linear Approximations" (2014) Downloads
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