Abstract
The results reported in this paper lend support to the nonparametric approach to estimating regression functions. This conclusion is based on a comparison of two sets of eight quarterly forecasts of U.S. hog supply generated by a well specified parametric dynamic model and by nonparametric kernel estimation. Despite the relatively small sample size, the nonparametric point forecasts are found to be as accurate as the parametric forecasts according to the mean square error and mean absolute error criteria. Bootstrap resampling is used to estimate the distributions of the forecast errors. The results of this exercise favour the nonparametric forecasts, which are found to have a tighter distribution.
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The authors are grateful to Aman Ullah for his very helpful comments on this paper. However, the authors remain responsible for any errors and limitations.
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Moschini, G., Prescott, D.M. & Stengos, T. Nonparametric kernel estimation applied to forecasting: An evaluation based on the bootstrap. Empirical Economics 13, 141–154 (1988). https://doi.org/10.1007/BF01972445
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DOI: https://doi.org/10.1007/BF01972445