Performs the Lagrange Multipliers test for homoscedasticity in a stationary process. The null hypothesis (H0), is that the process is homoscedastic.
Lm.test(y,lag.max = 2,alpha = 0.05)
a numeric vector or an object of the
an integer with the number of used lags.
Level of the test, possible values range from 0.01 to 0.1. By default
The Lagrange Multiplier test proposed by Engle (1982) fits a linear regression model for the squared residuals and examines whether the fitted model is significant. So the null hypothesis is that the squared residuals are a sequence of white noise, namely, the residuals are homoscedastic.
a h.test class with the main results of the Lagrage multiplier hypothesis test. The h.test class have the following values:
"Lm"The lagrange multiplier statistic
"df"The test degrees freedoms
"p.value"The p value
"alternative"The alternative hypothesis
"method"The used method
"data.name"The data name.
A. Trapletti and Asael Alonzo Matamoros
Engle, R. F. (1982). Auto-regressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica. 50(4), 987-1007.
McLeod, A. I. and W. K. Li. (1984). Diagnostic Checking ARMA Time Series Models Using Squared-Residual Auto-correlations. Journal of Time Series Analysis. 4, 269-273.
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