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Activity Number: 312 - Macroeconomic Forecasting in Theory and Practice
Type: Topic-Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #317628
Title: Evaluating the Federal Reserve’s Tealbook Forecasts
Author(s): Neil R Ericsson*
Companies: Federal Reserve Board
Keywords: machine learning; Federal Reserve; forecasts; GDP; foreign countries; United States
Abstract:

This paper examines publicly available Federal Reserve Board Tealbook forecasts of GDP growth for the United States and several foreign countries, focusing on potential time-varying biases and evaluating the Tealbook forecasts relative to other institutions’ forecasts. Tealbook forecasts perform relatively well at short horizons, but with significant heterogeneity across countries. Also, while standard Mincer-Zarnowitz tests typically fail to detect biases in the Tealbook forecasts, recently developed indicator saturation techniques that employ machine learning are able to detect economically sizable and highly significant time-varying biases. Estimated biases differ not only over time, but by country and across the forecast horizon. These biases point to directions for forecast improvement.


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