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Understanding exponential smoothing via kernel regression

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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.

Keywords: Bandwidth selection; Cross-validation; Dependent errors regression; Kernel smoothing; Limiting distribution; Local polynomial

Document Type: Original Article

DOI: http://dx.doi.org/10.1111/1467-9868.00161

Affiliations: 1: Université Catholique de Louvain, Louvain-la-Neuve, Belgium, 2: University of Newcastle, Callaghan, Australia, 3: University of New South Wales, Sydney, Australia

Publication date: January 1, 1999

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