This paper develops the approximate finite-sample bias of the ordinary least squares or quasi max- imum likelihood estimator of the mean reversion parameter in continuous-time Levy processes. For the special case of Gaussian processes, our results reduce to those of Tang and Chen (2009) (when...

Econometricians have recently been interested in estimating and testing the mean reversion parameter (κ) in linear diffusion models. It has been documented that the maximum likelihood estimator (MLE) of κ tends to over estimate the true value. Its asymptotic distribution, on the other...

This paper develops the approximate nite-sample bias of the ordinary least squares or quasi maximum likelihood estimator of the mean reversion parameter in continuous-time Levy processes. For the special case of Gaussian processes, our results reduce to those of Tang and Chen (2009) (when the...

This paper develops the approximate bias of the ordinary least squares estimator of the mean reversion parameter in continuous-time Lévy processes. Several cases are considered, depending on whether the long-run mean is known or unknown and whether the initial condition is fixed or random. The...

We derive the exact distribution of the maximum likelihood estimator of the mean reversion parameter (k) in the Ornstein-Uhlenbeck process by employing numerical integration via analytical evaluation of a joint characteristic function. Different scenarios are considered: known or unknown drift...

Recently Martins-Filho and Yao (J Multivar Anal 100:309–333, <CitationRef CitationID="CR7">2009</CitationRef>) have proposed a two-step estimator of nonparametric regression function with parametric error covariance and demonstrate that it is more efficient than the usual LLE. In the present paper we demonstrate that MY’s estimator...</citationref>