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Understanding exponential smoothing via kernel regression
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DOI：10.1111/1467-9868.00161.png)
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Abstract
En 中文
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
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IF:
3.6
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1.5K
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3.2W
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