The Finite-Sample Effects of VAR Dimensions on MLE Bias, MLE Variance and Minimum MSE Estimators: Purely Nonstationary Case
AbstractVector autoregressions (VAR's) are an important tool in time series analysis. However, relatively little is known about the finite-sample behaviour of parameter estimators. We address this issue here, by investigating maximum likelihood estimators (MLE's) in the context of a purely nonstationary first-order VAR. Using Monte Carlo simulation and numerical optimization, we derive response surfaces for MLE bias, in terms of VAR dimensions, given correct and over-parameterization of the model. We study non-zero initial values, and show that univariate bias nonmonotonicity disappears in the multivariate case. Lastly, we examine MLE variance and the correction factors required for the MLE to attain minimum mean squared error (MSE). Contact Details (for paper requests) : Dr. Steve Lawford ECARES Universite Libre de Bruxelles 50 Avenue F. D. Roosevelt CP 114 B-1050 Brussels Belgium Fax: +32 (0)2 650 4475 e-mail: firstname.lastname@example.org
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Bibliographic InfoPaper provided by Department of Economics, University of York in its series Discussion Papers with number 02/04.
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Postal: Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/economics/
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Finite sample bias; Monte Carlo simulation; Nonstationary time series; Response surfaces; Vector autoregression;
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