Evaluating Linear and Non-Linear Time-Varying Forecast-Combination Methods

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This paper evaluates linear and non-linear forecast-combination methods. Among the non-linear methods, we propose a nonparametric kernel-regression weighting approach that allows maximum flexibility of the weighting parameters. A Monte Carlo simulation study is performed to compare the performance of the different weighting schemes. The simulation results show that the non-linear combination methods are superior in all scenarios considered. When forecast errors are correlated across models, the nonparametric weighting scheme yields the lowest mean-squared errors. When no such correlation exists, forecasts combined using artificial neural networks are superior.

JEL Code(s): C, C1, C14, C5, C53, E, E2, E27

DOI: https://doi.org/10.34989/swp-2001-12