Finite-Sample Properties Of Forecasts From The Stationary First-Order Autoregressive Model Under A General Error Distribution
AbstractWe study the properties of the multi-period-ahead least-squares forecast for the stationary AR(1) model under a general error distribution. We find that the forecast is unbiased up to O(T 1), where T is the in-sample size, regardless of the error distribution and that the mean squared forecast error, up to O(T 3 2), is robust against nonnormality.The author is grateful to the co-editor Paolo Paruolo and two anonymous referees for helpful comments. The author is solely responsible for any remaining errors.
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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 23 (2007)
Issue (Month): 04 (August)
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- Pesaran, M. Hashem & Pick, Andreas & Timmermann, Allan, 2011.
"Variable selection, estimation and inference for multi-period forecasting problems,"
Journal of Econometrics,
Elsevier, vol. 164(1), pages 173-187, September.
- M. Hashem Pesaran & Andreas Pick & Allan Timmermann, 2010. "Variable Selection, Estimation and Inference for Multi-period Forecasting Problems," DNB Working Papers 250, Netherlands Central Bank, Research Department.
- Hännikäinen, Jari, 2014. "Multi-step forecasting in the presence of breaks," MPRA Paper 55816, University Library of Munich, Germany.
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