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Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy


This paper constructs hybrid forecasts that combine both short- and long-term conditioning information from external surveys with forecasts from a standard fixed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of variables, including those that are not directly tilted but are affected through spillover effects from tilted variables. The forecast accuracy gains for inflation are substantial, statistically significant, and are competitive with the forecast accuracy from both time-varying VARs and univariate benchmarks. We view our proposal as an indirect approach to accommodating structural change and moving end points.

JEL codes: E17, C53, C11, C32.
Keywords: Bayesian analysis, relative entropy, survey forecasts, nowcasts, density forecasts, real-time data.

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Suggested citation: Tallman, Ellis W. and Saeed Zaman, 2018. “Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy.” Federal Reserve Bank of Cleveland, Working Paper no. 18-09. https://doi.org/10.26509/frbc-wp-201809.

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