dwtest

Durbin-Watson test with linear regression model object

Syntax

``p = dwtest(mdl)``
``````p = dwtest(mdl,method)``````
``````p = dwtest(mdl,method,tail)``````
``````[p,DW] = dwtest(___)``````

Description

example

``p = dwtest(mdl)` returns the p-value of the Durbin-Watson Test on the residuals of the linear regression model `mdl`. The null hypothesis is that the residuals are uncorrelated, and the alternative hypothesis is that the residuals are autocorrelated.`
``````p = dwtest(mdl,method)``` specifies the algorithm for computing the p-value.```
``````p = dwtest(mdl,method,tail)``` specifies the alternative hypothesis.```
``````[p,DW] = dwtest(___)``` also returns the Durbin-Watson statistic using any of the input argument combinations in the previous syntaxes.```

Examples

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Determine whether a fitted linear regression model has autocorrelated residuals.

Load the `census` data set and create a linear regression model.

```load census mdl = fitlm(cdate,pop);```

Find the p-value of the Durbin-Watson autocorrelation test.

`p = dwtest(mdl)`
```p = 3.6190e-15 ```

The small p-value indicates that the residuals are autocorrelated.

Input Arguments

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Linear regression model, specified as a `LinearModel` object created using `fitlm` or `stepwiselm`.

Algorithm for computing the p-value, specified as one of these values:

• `'exact'` — Calculate an exact p-value using Pan’s algorithm [2].

• `'approximate'` — Calculate the p-value using a normal approximation [1].

The default is `'exact'` when the sample size is less than `400`, and `'approximate'` otherwise.

Type of alternative hypothesis to test, specified as one of these values:

ValueAlternative Hypothesis
`'both'`

Serial correlation is not 0.

`'right'`

Serial correlation is greater than 0 (right-tailed test).

`'left'`

Serial correlation is less than 0 (left-tailed test).

`dwtest` tests whether `mdl` has no serial correlation, against the specified alternative hypothesis.

Output Arguments

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p-value of the test, returned as a numeric value. `dwtest` tests whether the residuals are uncorrelated, against the alternative that autocorrelation exists among the residuals. A small p-value indicates that the residuals are autocorrelated.

Durbin-Watson statistic value, returned as a nonnegative numeric value.

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Durbin-Watson Test

The Durbin-Watson test tests the null hypothesis that linear regression residuals of time series data are uncorrelated, against the alternative hypothesis that autocorrelation exists.

The test statistic for the Durbin-Watson test is

`$DW=\frac{\sum _{i=1}^{n-1}{\left({r}_{i+1}-{r}_{i}\right)}^{2}}{\sum _{i=1}^{n}{r}_{i}^{2}},$`

where ri is the ith raw residual, and n is the number of observations.

The p-value of the Durbin-Watson test is the probability of observing a test statistic as extreme as, or more extreme than, the observed value under the null hypothesis. A significantly small p-value casts doubt on the validity of the null hypothesis and indicates autocorrelation among residuals.

References

[1] Durbin, J., and G. S. Watson. "Testing for Serial Correlation in Least Squares Regression I." Biometrika 37, pp. 409–428, 1950.

[2] Farebrother, R. W. Pan's "Procedure for the Tail Probabilities of the Durbin-Watson Statistic." Applied Statistics 29, pp. 224–227, 1980.

Version History

Introduced in R2012a