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Relevant bibliographies by topics / Durbin-Watson statistic

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Journal articles

Academic literature on the topic 'Durbin-Watson statistic'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Durbin-Watson statistic"

1

Champion, R., C. T. Lenard, and T. M. Mills. "Demonstrating the Durbin-Watson Statistic." Journal of the Royal Statistical Society: Series D (The Statistician) 47, no. 4 (1998): 643–44. http://dx.doi.org/10.1111/1467-9884.00161.

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2

Srivastava, M. S. "Asymptotic distribution of Durbin-Watson statistic." Economics Letters 24, no. 2 (1987): 157–60. http://dx.doi.org/10.1016/0165-1765(87)90243-6.

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3

Inder, Brett. "An Approximation to the Null Distribution of the Durbin-Watson Statistic in Models Containing Lagged Dependent Variables." Econometric Theory 2, no. 3 (1986): 413–28. http://dx.doi.org/10.1017/s0266466600011683.

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We consider testing for autoregressive disturbances in the linear regression model with a lagged dependent variable. An approximation to the null distribution of the Durbin—Watson statistic is developed using small-disturbance asymptotics, and is used to obtain test critical values. We also obtain nonsimilar critical values for the Durbin—Watson and Durbin's h and t tests. Monte Carlo results are reported comparing the performances of the tests under the null and alternative hypotheses. The Durbin–Watson test is found to be more powerful and to perform more consistently than either of Durbin's tests under Ho.

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4

Nakamura, Shisei, and Masanobu Taniguchi. "ASYMPTOTIC THEORY FOR THE DURBIN–WATSON STATISTIC UNDER LONG-MEMORY DEPENDENCE." Econometric Theory 15, no. 6 (1999): 847–66. http://dx.doi.org/10.1017/s0266466699156044.

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In time series regression models with “short-memory” residual processes, the Durbin–Watson statistic (DW) has been used for the problem of testing for independence of the residuals. In this paper we elucidate the asymptotics of DW for “long-memory” residual processes. A standardized Durbin–Watson statistic (SDW) is proposed. Then we derive the asymptotic distributions of SDW under both the null and local alternative hypotheses. Based on this result we evaluate the local power of SDW. Numerical studies for DW and SDW are given.

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5

Sheehan, Dennis. "Approximating the distribution of the durbin-watson statistic." Communications in Statistics - Theory and Methods 15, no. 1 (1986): 73–88. http://dx.doi.org/10.1080/03610928608829107.

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6

Bhargava, Alok. "Missing Observations and the Use of the Durbin-Watson Statistic." Biometrika 76, no. 4 (1989): 828. http://dx.doi.org/10.2307/2336649.

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7

Rutledge, D. N., and A. S. Barros. "Durbin–Watson statistic as a morphological estimator of information content." Analytica Chimica Acta 454, no. 2 (2002): 277–95. http://dx.doi.org/10.1016/s0003-2670(01)01555-0.

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8

BHARGAVA, ALOK. "Missing observations and the use of the Durbin–Watson statistic." Biometrika 76, no. 4 (1989): 828–31. http://dx.doi.org/10.1093/biomet/76.4.828.

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9

Tsay, W. "On the power of durbin-watson statistic against fractionally integrated processes." Econometric Reviews 17, no. 4 (1998): 361–86. http://dx.doi.org/10.1080/07474939808800423.

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10

Lieberman, Offer. "On the Approximation of Saddlepoint Expansions in Statistics." Econometric Theory 10, no. 5 (1994): 900–916. http://dx.doi.org/10.1017/s0266466600008914.

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The saddlepoint approximation as developed by Daniels [3] is an extremely accurate method for approximating probability distributions. Econometric and statistical applications of the technique to densities of statistics of interest are often hindered by the requirements of explicit knowledge of the c.g.f. and the need to obtain an analytical solution to the saddlepoint defining equation. In this paper, we show the conditions under which any approximation to the saddlepoint is justified and suggest a convenient solution. We illustrate with an approximate saddlepoint expansion of the Durbin-Watson test statistic.

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