strucchange (version 1.4-4)

recresid: Recursive Residuals

Description

A generic function for computing the recursive residuals (standardized one step prediction errors) of a linear regression model.

Usage

## S3 method for class 'default':
recresid(x, y, start = ncol(x) + 1, end = nrow(x),
  tol = sqrt(.Machine$double.eps), ...)
## S3 method for class 'formula':
recresid(formula, data = list(), ...)
## S3 method for class 'lm':
recresid(x, data = list(), ...)

Arguments

x, y, formula

specification of the linear regression model: either by a regressor matrix x and a response variable y, or by a formula or by a fitted object x of class "lm".

start, end

integer. Index of the first and last observation, respectively, for which recursive residuals should be computed. By default, the maximal range is selected.

tol

numeric. A relative tolerance for precision of recursive coefficient estimates, see details.

data

an optional data frame containing the variables in the model. By default the variables are taken from the environment which recresid is called from. Specifying data might also be necessary when applying recre

...

currently not used.

Value

• A vector containing the recursive residuals.

Details

Recursive residuals are standardized one-step-ahead prediction errors. Under the usual assumptions for the linear regression model they are (asymptotically) normal and i.i.d. (see Brown, Durbin, Evans, 1975, for details). The default method computes the initial coefficient estimates via QR decomposition, using lm.fit. In subsequent steps, the updating formula provided by Brown, Durbin, Evans (1975) is employed. To avoid numerical instabilities in the first steps (with typically small sample sizes), the QR solution is computed for comparison. When the relative difference between the two solutions falls below tol, only the updating formula is used in subsequent steps.

References

Brown R.L., Durbin J., Evans J.M. (1975), Techniques for testing constancy of regression relationships over time, Journal of the Royal Statistical Society, B, 37, 149-163.

See Also

efp

Examples

x <- rnorm(100) + rep(c(0, 2), each = 50)
rr <- recresid(x ~ 1)
plot(cumsum(rr), type = "l")

plot(efp(x ~ 1, type = "Rec-CUSUM"))