What is the Predictive Value of SPF Point and Density Forecasts?
This paper presents a new approach to combining the information in point and density forecasts from the Survey of Professional Forecasters (SPF) and assesses the incremental value of the density forecasts. Our starting point is a model, developed in companion work, that constructs quarterly term structures of expectations and uncertainty from SPF point forecasts for quarterly fixed horizons and annual fixed events. We then employ entropic tilting to bring the density forecast information contained in the SPF’s probability bins to bear on the model estimates. In a novel application of entropic tilting, we let the resulting predictive densities exactly replicate the SPF’s probability bins. Our empirical analysis of SPF forecasts of GDP growth and inflation shows that tilting to the SPF’s probability bins can visibly affect our model-based predictive distributions. Yet in historical evaluations, tilting does not offer consistent benefits to forecast accuracy relative to the model-based densities that are centered on the SPF’s point forecasts and reflect the historical behavior of SPF forecast errors. That said, there can be periods in which tilting to the bin information helps forecast accuracy.
Replication files are available at https://github.com/elmarmertens/ClarkGanicsMertensSPFfancharts
Clark, Todd E., Gergely Ganics, and Elmar Mertens. 2022. “What is the Predictive Value of SPF Point and Density Forecasts?” Federal Reserve Bank of Cleveland, Working Paper No. 22-37. https://doi.org/10.26509/frbc-wp-202237