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The RWDAR model: A novel state-space approach to forecasting

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  • Sbrana, Giacomo
  • Silvestrini, Andrea

Abstract

This paper introduces the Random Walk with Drift plus AutoRegressive model (RWDAR) for time-series forecasting. Owing to the presence of a random walk plus drift term, this model shares some similarities with the Theta model of Assimakopoulos and Nikolopoulos (2000). However, the addition of a first-order autoregressive term in the state equation provides additional adaptability and flexibility. Indeed, it is shown that RWDAR tends to outperform the Theta model when forecasting both stationary and nearly non-stationary time series. This paper also proposes a simple estimation method for the RWDAR model based on the solution of the algebraic Riccati equation for the prediction error covariance of the state vector. Simulation results show that this estimator performs as well as the standard Kalman filter approach. Finally, using yearly data from the M3 and M4 competition datasets, it is found that RWDAR outperforms traditional forecasting methods.

Suggested Citation

  • Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.
    • Handle: RePEc:eee:intfor:v:39:y:2023:i:2:p:922-937
    DOI: 10.1016/j.ijforecast.2022.03.003
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    References listed on IDEAS

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    More about this item

    Keywords

    Theta method; Kalman gain; Forecasting; M3 and M4 competitions; Approximate maximum likelihood estimation;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting

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