lmtest (version 0.9-37)

harvtest: Harvey-Collier Test

Description

Harvey-Collier test for linearity.

Usage

harvtest(formula, order.by = NULL, data = list())

Arguments

formula

a symbolic description for the model to be tested (or a fitted "lm" object).

order.by

Either a vector z or a formula with a single explanatory variable like ~ z. The observations in the model are ordered by the size of z. If set to NULL (the default) the observations are assumed to be ordered (e.g., a time series).

data

an optional data frame containing the variables in the model. By default the variables are taken from the environment which harvtest is called from.

Value

A list with class "htest" containing the following components:

statistic

the value of the test statistic.

p.value

the p-value of the test.

parameter

degrees of freedom.

method

a character string indicating what type of test was performed.

data.name

a character string giving the name(s) of the data.

Details

The Harvey-Collier test performs a t-test (with parameter degrees of freedom) on the recursive residuals. If the true relationship is not linear but convex or concave the mean of the recursive residuals should differ from 0 significantly.

Examples can not only be found on this page, but also on the help pages of the data sets bondyield, currencysubstitution, growthofmoney, moneydemand, unemployment, wages.

References

A. Harvey & P. Collier (1977), Testing for Functional Misspecification in Regression Analysis. Journal of Econometrics 6, 103--119

W. Kr<e4>mer & H. Sonnberger (1986), The Linear Regression Model under Test. Heidelberg: Physica

See Also

lm

Examples

# NOT RUN {
# generate a regressor and dependent variable
x <- 1:50
y1 <- 1 + x + rnorm(50)
y2 <- y1 + 0.3*x^2

## perform Harvey-Collier test
harv <- harvtest(y1 ~ x)
harv
## calculate critical value vor 0.05 level
qt(0.95, harv$parameter)
harvtest(y2 ~ x)
# }

Run the code above in your browser using DataCamp Workspace