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

Giacomo Sbrana and Andrea Silvestrini

International Journal of Forecasting, 2022, vol. 38, issue 1, 352-366

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.

Keywords: Random coefficient state-space model; Kalman gain; Forecasting; Forecast competitions; Approximate maximum likelihood (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:1:p:352-366

DOI: 10.1016/j.ijforecast.2021.06.003

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