MONETARY POLICY FORECASTING IN A DSGE MODEL WITH DATA THAT IS UNCERTAIN, UNBALANCED AND ABOUT THE FUTURE
Andres Gonzalez (),
Diego Rodriguez Guzman () and
Luis Rojas ()
BORRADORES DE ECONOMIA from BANCO DE LA REPÚBLICA
If theory-consistent models can ever hope to forecast well and to be useful for policy, theyhave to relate to data which though rich in information is uncertain, unbalanced and sometimes forecastsfrom external sources about the future path of other variables. One example from many is financial marketdata, which can help but only after smoothing out irrelevant short-term volatility. In this paper we proposecombining different types of useful but awkward data set with a linearised forward-looking DSGE modelthrough a Kalman Filter fixed-interval smoother to improve the utility of these models as policy tools. Weapply this scheme to a model for Colombia.
Keywords: Monetary Policy; DSGE; Forecast; Kalman Filter (search for similar items in EconPapers)
JEL-codes: C61 E01 F47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-dge, nep-ecm, nep-for, nep-mac and nep-mon
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Working Paper: Monetary Policy Forecasting in a DSGE Model with Data that is Uncertain, Unbalanced and About the Future (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:col:000094:005480
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