A new approach to forecasting based on exponential smoothing with independent regressors

Ahmad Osman () and Maxwell King

No 2/15, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In There is evidence that exponential smoothing methods as well as time varying parameter models perform relatively well in forecasting comparisons. The aim of this paper is to introduce a new forecasting technique by integrating the exponential smoothing model with regressors whose coefficients are time varying. In doing this, we construct an exponential smoothing model with regressors by extending Holt's linear exponential smoothing model. We then translate it into an equivalent state space structure so that the parameters can be estimated via the maximum likely-hood estimation procedure. Due to the potential problem in the updating equation for the regressor coefficients when the change in regressor is too small, we propose an alternative structure of the state space model which allows the updating process to be put on hold until sufficient information is available. An empirical study of forecast accuracy shows that the new model performs better than the existing exponential smoothing model as well as the linear regression model.

Keywords: State space model; Single source of error; Time varying parameter; Time series; Forecast accuracy (search for similar items in EconPapers)
JEL-codes: C51 C53 (search for similar items in EconPapers)
Pages: 20
Date: 2015
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
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