Estimating DSGE models with unknown data persistence
AbstractRecent empirical literature shows that key macro variables such as GDP and productivity display long memory dynamics. For DSGE models, we propose a Â‘GeneralizedÂ’ 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.
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Bibliographic InfoPaper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 750.
Date of creation: Mar 2010
Date of revision:
DSGE models; long memory; Kalman Filter.;
Find related papers by JEL classification:
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-04-17 (All new papers)
- NEP-DGE-2010-04-17 (Dynamic General Equilibrium)
- NEP-ECM-2010-04-17 (Econometrics)
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