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The accuracy of forecasts prepared for the Federal Open Market Committee

Andrew C. Chang and Tyler J. Hanson

Journal of Economics and Business, 2016, vol. 83, issue C, 23-43

Abstract: We analyze forecasts of consumption, nonresidential investment, residential investment, government spending, exports, imports, inventories, gross domestic product, inflation, and unemployment prepared by the staff of the Board of Governors of the Federal Reserve System for meetings of the Federal Open Market Committee from 1997 to 2008, called the Greenbooks. We compare the root mean squared error, mean absolute error, and the proportion of directional errors of Greenbook forecasts of these macroeconomic indicators with the errors from three forecasting benchmarks: a random walk, a first-order autoregressive model, and a Bayesian model averaged forecast from a suite of univariate time-series models commonly taught to first-year economics graduate students. We estimate our forecasting benchmarks both on end-of-sample vintage and real-time vintage data. We find that Greenbook forecasts significantly outperform our benchmark forecasts for horizons less than one quarter ahead. However, by the one-year forecast horizon, typically at least one of our forecasting benchmarks performs as well as Greenbook forecasts. Greenbook forecasts of personal consumption expenditures and unemployment tend to do relatively well, while Greenbook forecasts of inventory investment, government expenditures, and inflation tend to do poorly.

Keywords: Bayesian model averaging; Federal Open Market Committee; Forecast accuracy; Greenbook; National income and product accounts; Real-time data (search for similar items in EconPapers)
JEL-codes: C53 E17 E27 E37 F17 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jebusi:v:83:y:2016:i:c:p:23-43

DOI: 10.1016/j.jeconbus.2015.12.001

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