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.