Evaluating the Forecasting Power of an open-economy DSGE model when estimated in a Data-Rich Environment

This paper examines the inferences and forecasting benefits that can be made when one incorporates a large quantity of economic time series into international structural macroeconomic models. I estimate a close variation of Adolfson et al. (2007, 2008) small open-economy dynamic stochastic general equilibrium (DSGE) model in a data- rich environment and evaluate its predictive performance of the Canadian macroeconomy. The data set I use in the paper includes Canadian, American, Asian and European macro-financial data. I compare the forecasting performance of the DSGE model estimated in a data-rich environment (DSGE-DFM) to the forecasts generated by the DSGE model under estimated in its traditional setting and forecasts generated by other reduce formed forecasting models. I find that an open-economy DSGE model estimated in a data-rich environment significantly out performs its regularly estimated DSGE counterpart and the DSGE-DFM forecasts that incorporate real-time data are similar or better to the Bank of Canada’s Staff Economic Projections for GDP, consumption, investment, and trade statistics. In addition, the DSGE-DFM model of this paper is useful in forecasting both the real and nominal exchange rate in the short and medium-term.