Durbin-Watson Test for Autocorrelated Errors

Computes residual autocorrelations and generalized Durbin-Watson statistics and their bootstrapped p-values. dwt is an abbreviation for durbinWatsonTest.

Usage

durbinWatsonTest(model, ...)

dwt(...)

# S3 method for class 'lm'
durbinWatsonTest(model, max.lag=1, simulate=TRUE, reps=1000,
    method=c("resample","normal"),
    alternative=c("two.sided", "positive", "negative"), ...)

# Default S3 method
durbinWatsonTest(model, max.lag=1, ...)

# S3 method for class 'durbinWatsonTest'
print(x, ...)

Arguments

model

a linear-model object, or a vector of residuals from a linear model.

max.lag

maximum lag to which to compute residual autocorrelations and Durbin-Watson statistics.

simulate

if TRUE p-values will be estimated by bootstrapping.

reps

number of bootstrap replications.

method

bootstrap method: "resample" to resample from the observed residuals; "normal" to sample normally distributed errors with 0 mean and standard deviation equal to the standard error of the regression.

alternative

sign of autocorrelation in alternative hypothesis; specify only if max.lag = 1; if max.lag > 1, then alternative is taken to be "two.sided".

...

arguments to be passed down.

x

durbinWatsonTest object.

Value

Returns an object of type "durbinWatsonTest".

Note

p-values are available only from the lm method.

References

Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.

Author

John Fox jfox@mcmaster.ca

Examples

durbinWatsonTest(lm(fconvict ~ tfr + partic + degrees + mconvict, data=Hartnagel))
#>  lag Autocorrelation D-W Statistic p-value
#>    1        0.688345     0.6168636       0
#>  Alternative hypothesis: rho != 0