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

How to Cite

Gupta, A. K., Kabe, D., & Niwitpong, S. (2010). A Generalization of Durbin-Watson Statistic. European Journal of Pure and Applied Mathematics, 3(3), 435–442. Retrieved from https://www.ejpam.com/index.php/ejpam/article/view/800

Issue

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

Section

Special Issue on Granger Econometrics and Statistical Modeling

License

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European Journal of Pure and Applied Mathematics will be Copyright Holder.