# Is inconclusive region in durbin watson a problem?

What to do if my durbin watson is found to be inconclusive?

Do I have to change my model?

There are cases error serial correlation is a disaster. For example, if your model is $$y_t = \beta_0 + \beta_1 y_{t-1} + u_t$$, then serial correlation in $$u_t$$ means correlation of $$y_{t-1}$$ and $$u_t$$ (in general) and your OLS estimator is biased and inconsistent. In other cases, serial correlation does not cause endogeneity and OLS is still consistent. An example is the case the right-hand side variable is strictly exogenous. Then you can use OLS and heteroskedasticity and auto-correlation consistent (HAC) inferences, e.g., using the Newey-West standard errors.
If your model is dynamic ($$y_{t-1}$$ on the right-hand side), the chance is high that you need to seriously worry about serial correlation in $$u_t$$. This happens in many time-series and financial applications.
By the way, Farebrother (1980, JRSS Series C) proposed Pan's procedure for $$p$$ values for DW tests. Inconclusiveness can be avoided.