The use of bias correction versus the Jackknife when testing the mean reversion and long term mean parameters in continuous time models

Emma Iglesias and Phillips Garry D. A. ()
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Phillips Garry D. A.: Cardiff Business School, Cardiff University, CardiffCF10 3EU, United Kingdom

Monte Carlo Methods and Applications, 2017, vol. 23, issue 3, 159-164

Abstract: In this paper we extend the results in [5] in two directions: First, we show that by bias correcting the estimated mean reversion parameter we can also have better finite sample properties of the testing procedure using a t-statistic in the near unit root situation when the mean reversion parameter is approaching its lower bound versus using the Jackknife estimator of Phillips and Yu [8]. Second, we show that although Tang and Chen [10] demonstrate that the variance of the maximum likelihood estimator of the long term mean parameter is of an order equal to the reciprocal of the sample size (the same order as that of the bias and variance of the mean reversion parameter estimator and so it does not converge very fast to its true value), the t-statistic related to that parameter does not exhibit large empirical size distortions and so does not need to be bias corrected in practice.

Keywords: Least squares; maximum likelihood; continuous record; testing; bias correction; diffusion processes (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1515/mcma-2017-0111

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