durbinH: Calculate Durbin's h-statistic

In ecm: Build Error Correction Models

Calculate Durbin's h-statistic

Description

Calculates Durbin's h-statistic for autoregressive models.

Usage

durbinH(model, ylag1var)

Arguments

model	The model being assessed
ylag1var	The variable in the model that represents the lag of the y-term

Details

Using the Durbin-Watson (DW) test for autoregressive models is inappropriate because the DW test itself tests for first order autocorrelation. This doesn't apply to an ECM model, for which the DW test is still valid, but the durbinH function in included here in case an autoregressive model has been built. If Durbin's h-statistic is greater than 1.96, it is likely that autocorrelation exists.

Value

Numeric Durbin's h statistic

See Also

lm

Examples

##Not run

#Build a simple AR1 model to predict performance of the Wilshire 5000 Index
data(Wilshire)
Wilshire$Wilshire5000Lag1 <- c(NA, Wilshire$Wilshire5000[1:(nrow(Wilshire)-1)])
Wilshire <- Wilshire[complete.cases(Wilshire),]
AR1model <- lm(Wilshire5000 ~ Wilshire5000Lag1, data=Wilshire)

#Check Durbin's h-statistic on AR1model
durbinH(AR1model, "Wilshire5000Lag1")
#The h-statistic is 4.23, which means there is likely autocorrelation in the data.

ecm documentation built on Aug. 22, 2023, 9:09 a.m.