Monetary Policy Forecasting in a DSGE Model with Data that is Uncertain, Unbalanced and About the Future

Andres Gonzalez, Lavan Mahadeva, Diego Rodriguez Guzman () and Luis Rojas

Borradores de Economia from Banco de la Republica de Colombia

Abstract: If theory-consistent models can ever hope to forecast well and to be useful for policy, they have to relate to data which though rich in information is uncertain, unbalanced and sometimes forecasts from external sources about the future path of other variables. One example from many is financial market data, which can help but only after smoothing out irrelevant short-term volatility. In this paper we propose combining different types of useful but awkward data set with a linearised forward-looking DSGE model through a Kalman Filter fixed-interval smoother to improve the utility of these models as policy tools. We apply 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)
Date: 2009-04
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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https://doi.org/10.32468/be.559 (application/pdf)

Related works:
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:bdr:borrec:559

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