Simon M Potter: Models only get you so far Remarks by Mr Simon M Potter, Executive Vice President of the Markets Group of the Federal Reserve Bank of New York, for the Federal Reserve Banks of Atlanta and New York's First Annual Joint Research Day on Quantitative Tools for Monitoring Macroeconomic and Financial Conditions, Federal Reserve Bank of New York, New York City, 22 February 2019. * * * Accompanying Figures of the speech. As prepared for delivery It is a pleasure to deliver the lunchtime address during the Atlanta and New York Fed's first annual research day on quantitative tools for monitoring macroeconomic and financial conditions.1 My remarks will focus on some insights from the book Superforecasting by Philip Tetlock and Dan Gardner, with respect to how we interpret, use, and evaluate the results from our quantitative tools.2 If you have read the book, subtitled "The Art and Science of Prediction," you will probably be aware that it is much less mathematical than the discussions we have been having today, but at the same time is more complex in how it articulates the role of forecasting in good decision-making. Before I continue, I should note that these remarks reflect my own personal views and not necessarily those of the New York Fed or the Federal Reserve System. The insights in Superforecasting grew out of a forecasting tournament sponsored by the Intelligence Advanced Research Projects Activity (IARPA).
12 An early, working paper version of this research can be found in Marcelle Chauvet and Simon Potter,  Forecasting Recessions Using the Yield Curve, Federal Reserve Bank of New York Staff Reports, number 134, August 2001. 13 Brier scores measure probabilistic forecasting accuracy as a function of the distance between a probability  estimate and the actual outcome. For details on origin and calculation see “Verification of Forecasts Expressed in Terms of Probability,” Monthly Weather Review 78, no. 1, 1950. 14 See Michael D. Bauer and Thomas M. Mertens, Economic Forecasts with the Yield Curve, Federal Reserve Bank  of San Francisco Economic Letter, 2018–07, March 5, 2018. 15 Tetlock does discuss ambiguity aversion. Formalizing its role and that of asymmetric loss functions, seen for  instance in the work of Hansen and Sargent, is a good example where formal modeling can make significant advances over less structured judgmental approaches when it comes to decision-making based on forecasts. 16 The minutes to the July 31-August 1, 2018, FOMC meeting report on a Committee discussion about monetary  policy options at the zero lower bound. Although participants generally agreed that their current policy toolkit could provide significant accommodation, participants saw an apparent secular decline in neutral real interest rates as leaving less scope than in the past to reduce the federal funds rate in response to negative shocks, meaning spells at the ZLB could become more frequent and protracted than in the past.