Estimating DSGE Models with Unknown Data Persistence

46 Pages Posted: 1 Sep 2010

Date Written: March 22, 2010

Abstract

Recent empirical literature shows that key macro variables such as GDP and productivity display long memory dynamics. For DSGE models, we propose a ‘Generalized’ Kalman Filter to deal effectively with this problem: our method connects to and innovates upon data-filtering techniques already used in the DSGE literature. We show our method produces more plausible estimates of the deep parameters as well as more accurate out-of-sample forecasts of macroeconomic data.

Keywords: DSGE Models, Long Memory, Kalman Filter

JEL Classification: C51, C53, E37

Suggested Citation

Moretti, Gianluca and Nicoletti, Giulio, Estimating DSGE Models with Unknown Data Persistence (March 22, 2010). Bank of Italy Temi di Discussione (Working Paper) No. 750, Available at SSRN: https://ssrn.com/abstract=1670091 or http://dx.doi.org/10.2139/ssrn.1670091

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

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
37
Abstract Views
562
PlumX Metrics