English, Article, Journal or magazine article edition: Nonlinear Nonparametric Prediction of the 90-Day T-Bill Rate John Barkoulas; Christopher F. Baum; Joseph Onochie

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Bookmark: http://trove.nla.gov.au/version/73465
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Nonlinear Nonparametric Prediction of the 90-Day T-Bill Rate
Author
  • John Barkoulas
  • Christopher F. Baum
  • Joseph Onochie
Physical Description
  • preprint
Notes
  • 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). 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 of changes in the 90-day T-bill rate.
  • interest rates, T-bill rate, forecasting, long memory
  • RePEc:boc:bocoec:320
Language
  • English
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