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.
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Length: 26 pages Date of creation: 01 Jan 1996 Date of revision: Publication status: published, Review of Financial Economics, 1997, 6:2, 187-198. Handle: RePEc:boc:bocoec:320
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Find related papers by JEL classification: E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Determination of Interest Rates; Term Structure of Interest Rates C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
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