@article{Forecast-Combination:877,
      recid = {877},
      author = {Li, Fuchun and Tkacz, Greg},
      title = {Evaluating Linear and Non-Linear Time-Varying  Forecast-Combination Methods},
      publisher = {Bank of Canada},
      address = {2001},
      pages = {1 online resource (vi, 15 pages)},
      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.},
      url = {http://www.oar-rao.bank-banque-canada.ca/record/877},
      doi = {https://doi.org/10.34989/swp-2001-12},
}