The Problem of Inconclusive Regio in Autocorrelation Tests in Least Square Regression | ||||
The Egyptian Statistical Journal | ||||
Article 1, Volume 57, Issue 1, June 2013, Page 1-17 | ||||
Document Type: Original Article | ||||
DOI: 10.21608/esju.2013.314342 | ||||
View on SCiNiTO | ||||
Abstract | ||||
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. | ||||
Keywords | ||||
Autocorrelation; Durbin; Watson Test; Monte Carlo Simulation | ||||
Statistics Article View: 22 |
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