Estimating DGSE models with long memory dynamics

Moretti Gianluca and Nicoletti Giulio

No 2008037, Discussion Papers (ECON - Département des Sciences Economiques) from Université catholique de Louvain, Département des Sciences Economiques

Abstract: Recent literature clams that key variables such as aggregate productivity and inflation display long memory dynamics. We study the impllications of this high degree of persistence on the estimation of Dynamic Stochastic General Equilibrium (DGSE) 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 procedure, we mainly augment the state space, which deals with this problem. By the means of the augmented state space we can consistently estimate the model parameters as well as produce more accurate out-of-sample forecasts compared to the standard Kalman filter.

Pages: 37
Date: 2008-12-01
New Economics Papers: this item is included in nep-cba, nep-dge, nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:ctl:louvec:2008037

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