A Generalization of Durbin-Watson Statistic

Authors

  • Arjun K. Gupta Distinguished Professor, Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, USA
  • D.G. Kabe Professor Emeritus
  • S. Niwitpong Professor, Department of Applied Statistics, King Mongkut's University of Technology North Bangkok, Thailand

Keywords:

Additive outlier, AR(1), Predictor, Prediction interval, Unit toot test

Abstract

Two generalizations of the Durbin-Watson Statistic d, for testing that the serial correlation, in a given univariate normal regression model, is zero, to its multivariate counter part, are proposed. In the univariate case the moments of d are obtained in terms of generalized gamma functions. Our methodology is based on the generalized quadratic form central Wishart distribution.

Author Biographies

  • Arjun K. Gupta, Distinguished Professor, Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, USA

    Distinguished Professor

  • S. Niwitpong, Professor, Department of Applied Statistics, King Mongkut's University of Technology North Bangkok, Thailand
    Professor, Department of Applied Statistics, King Mongkut's University of Technology North Bangkok, Thailand

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Published

2010-05-22

Issue

Vol. 3 No. 3 (2010): Special Issue on Granger Econometrics and Statistical Modeling

Section

Special Issue on Granger Econometrics and Statistical Modeling

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How to Cite

A Generalization of Durbin-Watson Statistic. (2010). European Journal of Pure and Applied Mathematics, 3(3), 435-442. https://www.ejpam.com/index.php/ejpam/article/view/800