Nonlinear Nonparametric Prediction of the 90-Day T-Bill Rate

John Barkoulas, Christopher Baum and Joseph Onochie
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Joseph Onochie: Baruch College

No 320., Boston College Working Papers in Economics from Boston College Department of Economics

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). 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.

Keywords: interest rates; T-bill rate; forecasting; long memory (search for similar items in EconPapers)
JEL-codes: C22 C52 E43 (search for similar items in EconPapers)
Pages: 26 pages
Date: 1996-01-01
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Published, Review of Financial Economics, 1997, 6:2, 187-198.

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Persistent link: https://EconPapers.repec.org/RePEc:boc:bocoec:320

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