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

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.

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Authors:	Gómez, Andrés González ; Mahadeva, Lavan ; Rodríguez, Diego ; Rojas, Luis Eduardo
Institutions:	Banco de la Republica de Colombia
Subject:	Monetary Policy \| DSGE \| Forecast \| Kalman Filter Classification

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Extent:	application/pdf
Series:	Borradores de Economia.
Type of publication:	Book / Working Paper
Language:	English
Classification:	F47 - Forecasting and Simulation ; E01 - Measurement and Data on National Income and Product Accounts and Wealth ; C61 - Optimization Techniques; Programming Models; Dynamic Analysis
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10004991070