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Forecasts from Reduced-form Models under the Zero-Lower-Bound Constraint

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  • Mehmet Pasaogullari

Abstract

In this paper, I consider forecasting from a reduced-form VAR under the zero lower bound (ZLB) for the short-term nominal interest rate. I develop a method that a) computes the exact moments for the first n + 1 periods when n previous periods are tracked and b) approximates moments for the periods beyond n + 1 period using techniques for truncated normal distributions and approximations a la Kim (1994). I show that the algorithm produces satisfactory results for VAR systems with moderate to high persistence even when only one previous period is tracked. For very persistent VAR systems, however, tracking more periods is needed in order to obtain reliable approximations. I also show that the method is suitable for affine term-structure modeling, where the underlying state vector includes the short-term interest rate as in Taylor rules with inertia.

Suggested Citation

  • Mehmet Pasaogullari, 2015. "Forecasts from Reduced-form Models under the Zero-Lower-Bound Constraint," Working Papers (Old Series) 1512, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1512
    DOI: 10.26509/frbc-wp-201512
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    monetary policy; forecasting from VARs; zero lower bound; normal mixtures;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • 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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