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Title: Risk-sensitive linear approximations Authors:  Alexander Meyer-Gohde - Hamburg University (Germany) [presenting]
Abstract: Risk-sensitive approximations are constructed for policy functions of DSGE models around the stochastic steady state and ergodic mean that are linear in state variables. The method requires only the solution of linear equations using standard perturbation output to construct the approximation and is uniformly more accurate than standard linear approximations. In an application to real business cycles with recursive utility and long-run and volatility risk, the approximation successfully estimates risk aversion and stochastic volatility using the Kalman filter, where a standard linear approximation provides no information and alternative methods require computationally intensive procedures such as particle filters. At the posterior mode, the models asset pricing implications are brought in line with postwar US data without compromising the fit of the macroeconomy.