The Durbin-Watson (DW) test is one of the most widely used tests for autocorrelation in regression models. The DW test has, however, an important limitation: the test is indeterminate when the test statistic falls into the so-called Inconclusive (Ignorance) Region. The Inconclusive Region exists because the true distribution of the DW statistic is not tractable. Though there have been suggested a number of approximation methods to establish more accurate critical values for the DW test. This paper compares between the three tests of Inconclusive Region of DW test using a Monte Carlo study, these tests are Theil-Nagar (1961) (TN), Henshaw (1966) (H) and Durbin-Watson (1971). This paper concludes that H considerably outperforms the original DW test and identical to Durbin-Watson (1971) approximation.
Morad, N., & Najem Obaid, A. (2013). The Problem of Inconclusive Region in Autocorrelation Tests in Least Square Regression. The Egyptian Statistical Journal, 57(1), 1-17. doi: 10.21608/esju.2013.314342
MLA
Naglaa A. Morad; Ameera Najem Obaid. "The Problem of Inconclusive Region in Autocorrelation Tests in Least Square Regression", The Egyptian Statistical Journal, 57, 1, 2013, 1-17. doi: 10.21608/esju.2013.314342
HARVARD
Morad, N., Najem Obaid, A. (2013). 'The Problem of Inconclusive Region in Autocorrelation Tests in Least Square Regression', The Egyptian Statistical Journal, 57(1), pp. 1-17. doi: 10.21608/esju.2013.314342
VANCOUVER
Morad, N., Najem Obaid, A. The Problem of Inconclusive Region in Autocorrelation Tests in Least Square Regression. The Egyptian Statistical Journal, 2013; 57(1): 1-17. doi: 10.21608/esju.2013.314342
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