<p>There are cases error serial correlation is a disaster. For example, if your model is <span class="math-container">$y_t = \beta_0 + \beta_1 y_{t-1} + u_t$</span>, then serial correlation in <span class="math-container">$u_t$</span> means correlation of <span class="math-container">$y_{t-1}$</span> and <span class="math-container">$u_t$</span> (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.</p>

<p><strong>So my answer is "it depends".</strong> If some regressors become endogenous due to the error serial correlation, you will probably want to change your model. Otherwise, you can use OLS and HAC standard errors.</p>

<p>If your model is dynamic (<span class="math-container">$y_{t-1}$</span> on the right-hand side), the chance is high that you need to seriously worry about serial correlation in <span class="math-container">$u_t$</span>. This happens in many time-series and financial applications.</p>

<p>By the way, <a href="https://www.jstor.org/stable/2986316" rel="nofollow noreferrer">Farebrother (1980, JRSS Series C)</a> proposed Pan's procedure for <span class="math-container">$p$</span> values for DW tests. Inconclusiveness can be avoided.</p>

CC BY-SA 4.0
2019-04-10T06:55:55.370Z
Thank you so much @chan1142. I think I'll just have to remodel it. 
Ry G
CC BY-SA 4.0
2019-04-09T12:24:57.880Z
2019-04-09T12:35:01.867Z
2019-04-09T12:35:01.867Z
chan1142
<p>What to do if my durbin watson is found to be inconclusive? </p>

<p>Do I have to change my model? </p>

<econometrics>
Is inconclusive region in durbin watson a problem?
Ry G
# Is inconclusive region in durbin watson a problem?

(Asked by: username_0 on 2019-04-09)

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

Do I have to change my model?

**Tags:** econometrics


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## 1 answers

### Answer by username_1 on 2019-04-09. Score: 3 | Accepted answer

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.

**So my answer is "it depends".** If some regressors become endogenous due to the error serial correlation, you will probably want to change your model. Otherwise, you can use OLS and HAC 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)](https://www.jstor.org/stable/2986316) proposed Pan's procedure for $p$ values for DW tests. Inconclusiveness can be avoided.

> **Comments:**
> **username_0**: Thank you so much @username_1. I think I'll just have to remodel it.   

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