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: 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
Do you have negative results from your research you’d like to share?
Feedback
Feedback to SSRN
If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday.