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Estimating DSGE models with persistent dynamics

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 modication of the Kalman Filter which deals with this problem. We show then that the modied Kalman Filter procedure can beat the standard one in a real-data out-of-sample forecasting exercise.