The Problem of Inconclusive Region in Autocorrelation Tests in Least Square Regression | ||
| The Egyptian Statistical Journal | ||
| Article 1, Volume 57, Issue 1, June 2013, Pages 1-17 PDF (19.1 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/esju.2013.314342 | ||
| Authors | ||
| Naglaa A. Morad* 1; Ameera Najem Obaid2 | ||
| 1Department of Applied Statistics and Econometrics, Institute of Statistical Studies and Research, Cairo University, Egypt | ||
| 2M.Sc Student | ||
| 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 | ||
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