Estimating DSGE models with persistent dynamics
نویسندگان
چکیده
Recent literature points out that key variables such as aggregate productivity and ination display long memory dynamics. We study the implications of this high degree of persistence on the estimation of Dynamic Stochastic General Equilibrium (DSGE) models. We rst show that long memory data can produce substantial bias in the deep parameter estimates when a standard Kalman Filter-MLE procedure is used; therefore we propose an original modi cation of the Kalman Filter which deals with this problem. We show then that the modi ed Kalman Filter procedure can beat the standard one in a real-data out-of-sample forecasting exercise.
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