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Title: Multistep prediction error decomposition in DSGE models: Estimation and forecast performance Authors:  Simon Price - University of Essex (United Kingdom) [presenting]
George Kapetanios - Kings College, University of London (United Kingdom)
Konstantinos Theodoridis - Bank of England (United Kingdom)
Abstract: DSGE models are of interest because they offer structural interpretations, but are also increasingly used for forecasting. Estimation often proceeds by methods which involve building the likelihood by one-step ahead ($h=1$) prediction errors. However in principle this can be done using different horizons where $h>1$. Using a well-known model, for $h=1$ classical ML parameter estimates are similar to those originally reported. As $h$ extends some estimated parameters change, but not to an economically significant degree. Forecast performance is often improved, in several cases significantly.