Abstract
In the case where the lagged dependent variables are included in the regression model, it is known that the ordinary least squares estimates (OLSE) are biased in small sample and that the bias increases as the number of the irrelevant variables increases. In this paper, based on the bootstrap methods, an attempt is made to obtain the unbiased estimates in autoregressive and non-Gaussian cases. We propose the residual-based bootstrap method in this paper. Some simulation studies are performed to examine whether the proposed estimation procedure works well or not. We obtain the results that it is possible to recover the true parameter values and that the proposed procedure gives us the less biased estimators than OLSE.
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This paper is a substantial revision of Tanizaki (2000). The normality assumption is adopted in Tanizaki (2000), but it is not required in this paper. The authors are grateful to an anonymous referee for valuable suggestions and comments. This research was partially supported by Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (C)(2) #14530033, 2002–2005, for H. Tanizaki and Grants-in-Aid for the 21st Century COE Program.
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Tanizaki, H., Hamori, S. & Matsubayashi, Y. On least-squares bias in the AR(p) models: Bias correction using the bootstrap methods. Statistical Papers 47, 109–124 (2006). https://doi.org/10.1007/s00362-005-0275-6
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DOI: https://doi.org/10.1007/s00362-005-0275-6