Accelerating the pace of engineering and science

dwtest

Class: LinearModel

Durbin-Watson test of linear model

Syntax

P = dwtest(mdl)
[P,DW] = dwtest(mdl)
[P,DW] = dwtest(mdl,method)
[P,DW] = dwtest(mdl,method,tail)

Description

P = dwtest(mdl) returns the p-value of the Durbin-Watson test on the mdl linear model.

[P,DW] = dwtest(mdl) returns the Durbin-Watson statistic.

[P,DW] = dwtest(mdl,method) specifies the method dwtest uses to compute the p-value.

[P,DW] = dwtest(mdl,method,tail) specifies the alternative hypothesis.

Input Arguments

mdl

Linear model, as constructed by fitlm or stepwiselm.

method

Algorithm for computing the p-value:

• 'exact' — Calculates an exact p-value using Pan's algorithm.

• 'approximate' — Calculates the p-value using a normal approximation.

Default: 'exact' when the sample size is less than 400, 'approximate' otherwise

tail

dwtest tests whether mdl has no serial correlation against one of these alternative hypotheses:

TailAlternative 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).

Default: 'both'

Output Arguments

 P p-value of the test, a scalar. dwtest tests if the residuals are uncorrelated, against the alternative that there is autocorrelation among them. Small values of P indicate that the residuals are correlated. DW Value of the Durbin-Watson statistic, a scalar.

Definitions

Durbin-Watson Statistic

Let r be the vector of residuals (in mdl.residuals.response). The Durbin-Watson statistic is

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

Examples

expand all

Test Residuals for Autocorrelation

Examine whether the residuals from a fitted model of census data over time have autocorrelated residuals.

Load the census data and create a linear model.

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

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

`P = dwtest(mdl)`
```P =

0```

There is significant autocorrelation in the residuals.

Algorithms

Approximate calculation of the p-value uses a normal approximation [1]. Exact calculation uses Pan's algorithm [2].

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.