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

Fuchun Li and Greg Tkacz ()

Staff Working Papers from Bank of Canada

Abstract: 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.

Keywords: Econometric; and; statistical; methods (search for similar items in EconPapers)
JEL-codes: C14 C53 E27 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2001
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:01-12

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