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Estimating DSGE models with unknown data persistence

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  • Gianluca Moretti

    ()
    (Bank of Italy)

  • Giulio Nicoletti

    ()
    (Bank of Italy)

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    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.

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    File URL: http://www.bancaditalia.it/pubblicazioni/econo/temidi/td10/td750_10/en_td_750_10/en_tema_750.pdf
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    Bibliographic Info

    Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 750.

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    Date of creation: Mar 2010
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    Handle: RePEc:bdi:wptemi:td_750_10

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    Keywords: DSGE models; long memory; Kalman Filter.;

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