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Estimating DSGE models with unknown data persistence

Author

Listed:
  • Gianluca Moretti

    (Bank of Italy)

  • Giulio Nicoletti

    (Bank of Italy)

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.

Suggested Citation

  • Gianluca Moretti & Giulio Nicoletti, 2010. "Estimating DSGE models with unknown data persistence," Temi di discussione (Economic working papers) 750, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_750_10
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    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2010/2010-0750/en_Tema_750.pdf
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    References listed on IDEAS

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    Cited by:

    1. Raffaella Giacomini, 2015. "Economic theory and forecasting: lessons from the literature," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 22-41, June.

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    More about this item

    Keywords

    DSGE models; long memory; Kalman Filter.;
    All these keywords.

    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

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