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      Nearest-Neighbor Forecasts of U.S Interest Rates

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          Abstract

          We employ a nonlinear, nonparametric method to model the stochastic behavior of changes in several short and long term U.S interest rates. We apply a nonlinear autoregression to the series using the locally weighted regression (LWR) estimation method, a nearest-neighbor method, and evaluate the forecasting performance with a measure of root mean square error (RMSE). We compare the forecasting performance of the nonparametric fit to the performance of two benchmark linear model: an autoregressive model and a random-walk-with-drift model. The nonparametric model exhibits greater out-of-sample forecast accuracy that of the linear predictors for most U.S interest rate series. The improvements in forecast accuracy are statistically significant and robust. This evidence establishes the presence of significant nonlinear mean predictability in U.S interest rates, as well as the usefulness of the LWR method as modeling strategy for these benchmark series    

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          Author and article information

          Contributors
          United States
          United States
          United States
          Journal
          International Journal of Banking and Finance
          UUM Press
          March 17 2003
          : 1
          : 119-140
          Affiliations
          [1 ]University of Tennessee
          Article
          8331
          10.32890/ijbf2003.1.1.8331
          af3d42b4-b241-4b8c-a34f-b63010795e4f

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          History

          General economics,Financial economics,International economics & Trade,Industrial organization,Macroeconomics,Microeconomics

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