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Forecast Performance of Noncausal Autoregressions and the Importance of Unit Root Pretesting

Author

Listed:
  • Frédérique Bec

    (THEMA - Théorie économique, modélisation et applications - UCP - Université de Cergy Pontoise - Université Paris-Seine - CNRS - Centre National de la Recherche Scientifique, CREST - Center for Research in Extreme Scale Technologies [Bloomington] - Indiana University [Bloomington] - Indiana University System)

  • Heino Bohn Nielsen

    (UCPH - University of Copenhagen = Københavns Universitet)

Abstract

Based on large a simulation study, this paper investigates which strategy to adopt in order to choose the most accurate forecasting model for Mixed causal-noncausal AutoRegressions (MAR) data generating processes: always differencing (D), never differencing (L) or unit root pretesting (P). Relying on recent econometric developments regarding forecasting and unit root testing in the MAR framework, the main results suggest that from a practitioner's point of view, the P strategy at the 10%-level is a good compromise. In fact, it never departs too much from the best model in terms of forecast accuracy, unlike the L (respectively D) strategy when the DGP becomes very persistent (respectively less persistent).

Suggested Citation

  • Frédérique Bec & Heino Bohn Nielsen, 2023. "Forecast Performance of Noncausal Autoregressions and the Importance of Unit Root Pretesting," Working Papers hal-04316071, HAL.
    • Handle: RePEc:hal:wpaper:hal-04316071
    Note: View the original document on HAL open archive server: https://hal.science/hal-04316071
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