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Forecast accuracy and effort: The case of US inflation rates

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  • Nicholas Taylor

Abstract

This paper investigates the relationship between forecast accuracy and effort, where effort is defined as the number of times the model used to generate forecasts is recursively estimated over the full sample period. More specifically, within a framework of costly effort, optimal effort strategies are derived under the assumption that the dynamics of the variable of interest follow an autoregressive-type process. Results indicate that the strategies are fairly robust over a wide range of linear and nonlinear processes (including structural break processes), and deliver forecasts of transitory, core and total inflation that require less effort to generate and are as accurate as (that is, are insignificantly different from) those produced with maximum effort. Copyright (C) 2010 John Wiley & Sons, Ltd.

Suggested Citation

  • Nicholas Taylor, 2011. "Forecast accuracy and effort: The case of US inflation rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 644-665, November.
    • Handle: RePEc:jof:jforec:v:30:y:2011:i:7:p:644-665
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    File URL: http://hdl.handle.net/10.1002/for.1199
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    Cited by:

    1. Chang Liu & Raja Nassar & Min Guo, 2015. "A Method of Retail Mortgage Stress Testing: Based on Time‐Frame and Magnitude Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 261-274, July.
    2. Pramesti Getut, 2023. "Parameter least-squares estimation for time-inhomogeneous Ornstein–Uhlenbeck process," Monte Carlo Methods and Applications, De Gruyter, vol. 29(1), pages 1-32, March.

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