Exponential Smoothing and the Akaike Information Criterion
Ralph Snyder () and
Keith Ord ()
No 4/09, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
Using an innovations state space approach, it has been found that the Akaike information criterion (AIC) works slightly better, on average, than prediction validation on withheld data, for choosing between the various common methods of exponential smoothing for forecasting. There is, however, a puzzle. Should the count of the seed states be incorporated into the penalty term in the AIC formula? We examine arguments for and against this practice in an attempt to find an acceptable resolution of this question.
Keywords: Exponential smoothing; forecasting; Akaike information criterion; innovations state space approach (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
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