Estimating DSGE Models with Long Memory Dynamics

23 Pages Posted: 28 May 2008

Date Written: 2008-3

Abstract

Recent literature claims that key variables such as aggregate productivity and inflation 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 show that long memory data produce substantial bias in the deep parameter estimates when a standard Kalman Filter-MLE procedure is used. We propose a modification of the Kalman Filter to effectively deal with this problem. The augmented Kalman Filter can consistently estimate the model parameters as well as produce more accurate out-of-sample forecasts compared to the standard Kalman filter.

Keywords: DSGE models, long memory

JEL Classification: C51, C52, E37

Suggested Citation

Moretti, Gianluca and Nicoletti, Giulio, Estimating DSGE Models with Long Memory Dynamics (2008-3). Available at SSRN: https://ssrn.com/abstract=1137529 or http://dx.doi.org/10.2139/ssrn.1137529

Gianluca Moretti

Bank of Italy ( email )

Via Nazionale 91
00184 Roma
Italy

Giulio Nicoletti (Contact Author)

European Central Bank ( email )

Kaiserstrasse 29
Frankfurt am Main, Hessen 60311
Germany

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