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Random coefficient state-space model: Estimation and performance in M3–M4 competitions

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  • Sbrana, Giacomo
  • Silvestrini, Andrea

Abstract

The random coefficient state-space model was first introduced by McKenzie and Gardner (2010). This model is a stochastic combination of simple and double exponential smoothing, a desirable feature for time-series forecasting. This paper provides a simple method to estimate the random coefficient state-space model parameters by exploiting the link between the model’s autocovariance and the Kalman filter. A simulation exercise shows that the proposed estimator has good finite-sample properties. This paper also evaluates the model’s forecasting performance in large-scale empirical applications, which is remarkable. Indeed, this model outperforms all competing (not-combined) benchmarks when using the yearly data from the M3 competition dataset. Furthermore, employing the yearly data from the M4 competition, it continues to beat its competitors, with a performance comparable to that of the Theta method. The predictive performance is assessed using both the MASE/sMAPE metrics and the Model Confidence Set procedure.

Suggested Citation

  • Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:1:p:352-366
    DOI: 10.1016/j.ijforecast.2021.06.003
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