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.
This work represents the opinions of the authors alone and not the Board members of the Banco de la República.
This content has been translated into English for informational purposes. Upon any query regarding its interpretation or enforceability, the Spanish version shall be deemed official, and will prevail over the English version. The authors of specific texts such as working papers or articles select the language of publication; therefore, there might be cases in which the content may only be available in English.