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A nonparametric investigation of the 90-day t-bill rate

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  • Barkoulas, John T.
  • Baum, Christopher F.
  • Onochie, Joseph

Abstract

We employ a nonlinear, nonparametric method to model the stochastic behavior of changes in the 90‐day U.S. T‐bill rate. The estimation technique is locally weighted regression (LWR), a nearest‐neighbor method, and the forecasting criteria are the root mean square error (RMSE) and mean absolute deviation (MAD) measures. We compare the forecasting performance of the nonparametric fit to the performance of two benchmark linear models: an autoregressive model and a random‐walk‐with‐drift model. The nonparametric fit results in significant improvements in forecasting accuracy as compared to benchmark linear models both in‐sample and out‐of‐sample, thus establishing the presence of substantial nonlinear mean predictability in the 90‐day T‐bill rate.
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  • Barkoulas, John T. & Baum, Christopher F. & Onochie, Joseph, 1997. "A nonparametric investigation of the 90-day t-bill rate," Review of Financial Economics, Elsevier, vol. 6(2), pages 187-198.
    • Handle: RePEc:eee:revfin:v:6:y:1997:i:2:p:187-198
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    3. Tierney, Heather L.R., 2011. "Forecasting and tracking real-time data revisions in inflation persistence," MPRA Paper 34439, University Library of Munich, Germany.
    4. Heather L. R. Tierney, 2019. "Forecasting with the Nonparametric Exclusion-from-Core Inflation Persistence Model Using Real-Time Data," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(1), pages 39-63, February.

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