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FORECAST PERFORMANCE OF NON-CAUSAL AUTOREGRESSIONS AND THE IMPORTANCE OF UNIT ROOT PRETESTING
1 : Centre de Recherche en Économie et Statistique
(CREST)
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Website
Centre National de la Recherche Scientifique : UMR9194
5, Avenue Henry Le Chatelier91120 Palaiseau -
France
2 : Théorie économique, modélisation et applications
(THEMA)
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Website
Université de Cergy Pontoise : Théorie économique, modélisation et applications, Centre National de la Recherche Scientifique : UMR8184
33, boulevard du Port 95011 Cergy-Pontoise Cedex -
France
Based on large simulations study, this paper investigates which strategy to adopt in order to choose the best forecasting model --- in terms of accuracy --- 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 this MAR framework, the main results suggest that from a practitioner's point of view, the P strategy at the 1\%-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 with little persistence).
