Forecasting FOMC Forecasts

Sarp Kalfa and Jaime Marquez ()
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Jaime Marquez: Johns Hopkins School of Advanced International Studies (SAIS)

No 2018-007, Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting

Abstract: Summarizing Hendry’s forty years of work on taming uncertainty is "clear and distinct": Test, test, test. Sure - but test what? Test the maintained assumptions of the disturbances. Test the parameter restrictions of a given model. Test the explanatory power of a model against a rival model. In brief, test everything that is not clear and distinct. We implement Hendry’s view to forecast FOMC forecasts. Specifically, monetary policy is forward looking and, in its pursuit of transparency, it communicates its economic projections to the public at large. As a result, there is interest in whether these projections are credible. We argue that central to that credibility is the public’s ability to replicate FOMC’s projections using publicly available data only. In other words, is it possible to anticipate, reliably and independently, what the FOMC will anticipate for the federal funds rate? To address this question, we assemble FOMC projections from 1992 to 2017; examine their statistical properties; postulate models to predict FOMC projections; estimate the parameters of these models; and generate out-of-sample predictions for inflation, unemployment, and the federal funds rate for 2018. As the reader will soon realize, there is a lot more testing to be done.

Keywords: Autometrics; Federal Funds Rate; FOMC; Survey of Professional Forecasters (search for similar items in EconPapers)
JEL-codes: C4 E5 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2018-11
New Economics Papers: this item is included in nep-cba, nep-for, nep-mac and nep-mon
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https://www2.gwu.edu/~forcpgm/2018-007.pdf First version, 2018 (application/pdf)

Related works:
Journal Article: Forecasting FOMC Forecasts (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:gwc:wpaper:2018-007

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