Forecasting with Shadow-Rate VARs

Andrea Carriero, Todd Clark, Massimiliano Marcellino and Elmar Mertens

No 21-09, Working Papers from Federal Reserve Bank of Cleveland

Abstract: Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables. A popular class of forecasting models is linear vector autoregressions (VARs) that include shorter- and longer-term interest rates. However, in a number of economies, at least shorter-term interest rates have now been stuck for years at or near their effective lower bound (ELB), with longer-term rates drifting toward the constraint as well. In such an environment, linear forecasting models that ignore the ELB constraint on nominal interest rates appear inept. To handle the ELB on interest rates, we model observed rates as censored observations of a latent shadow-rate process in an otherwise standard VAR setup. The shadow rates are assumed to be equal to observed rates when above the ELB. Point and density forecasts for interest rates (short term and long term) constructed from a shadow-rate VAR for the US since 2009 are superior to predictions from a standard VAR that ignores the ELB. For other indicators of financial conditions and measures of economic activity and inflation, the accuracy of forecasts from our shadow-rate specification is on par with a standard VAR that ignores the ELB.

Keywords: Macroeconomic forecasting; effective lower bound; term structures; censored observations (search for similar items in EconPapers)
JEL-codes: C34 C53 E17 E37 E43 E47 (search for similar items in EconPapers)
Pages: 56
Date: 2021-03-29
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mac and nep-mon
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Citations: View citations in EconPapers (1)

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DOI: 10.26509/frbc-wp-202109

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