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Forecasting with Shadow-Rate VARs

Author

Listed:
  • Andrea Carriero
  • Todd E. Clark
  • Massimiliano Marcellino
  • Elmar Mertens
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.

Suggested Citation

  • Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Forecasting with Shadow-Rate VARs," Working Papers 21-09, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwq:91780
    DOI: 10.26509/frbc-wp-202109
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    Cited by:

    1. Aruoba, S. Borağan & Mlikota, Marko & Schorfheide, Frank & Villalvazo, Sergio, 2022. "SVARs with occasionally-binding constraints," Journal of Econometrics, Elsevier, vol. 231(2), pages 477-499.

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    More about this item

    Keywords

    Macroeconomic forecasting; effective lower bound; term structures; censored observations;
    All these keywords.

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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