Understanding exponential smoothing via kernel regression

I. Gijbels, A. Pope, M. P. Wand

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45 Citations (Scopus)

Abstract

Exponential smoothing is the most common model-free means of forecasting a future realization of a time series. It requires the specification of a smoothing factor which is usually chosen from the data to minimize the average squared residual of previous one-step-ahead forecasts. In this paper we show that exponential smoothing can be put into a nonparametric regression framework and gain some interesting insights into its performance through this interpretation. We also use theoretical developments from the kernel regression field to derive, for the first time, asymptotic properties of exponential smoothing forecasters.

Original languageEnglish
Pages (from-to)39-50
Number of pages12
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume61
Issue number1
DOIs
Publication statusPublished - 1 Jan 1999
Externally publishedYes

Keywords

  • Bandwidth selection
  • Cross-validation
  • Dependent errors regression
  • Kernel smoothing
  • Limiting distribution
  • Local polynomial

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