MARX (version 0.2)

mixed: The MARX estimation function

Description

This function allows you to estimate mixed causal-noncausal MARX models by t-MLE (compatible with most functions in lm() class).

Usage

mixed(y, x, p_C, p_NC)
# S3 method for default
mixed(y, x, p_C, p_NC)
# S3 method for mixed
print(x, ...)
# S3 method for mixed
summary(object, ...)

Arguments

y

Data vector of time series observations.

x

Matrix of data (every column represents one time series). Specify NULL or "not" if not wanted.

p_C

Number of lags to be included.

p_NC

Number of leads to be included.

...

Other parameters.

object

An object of the class "mixed".

Value

An object of class "mixed" is a list containing the following components:

coefficients

Vector of estimated coefficients.

se

Standard errors of estimated coefficients.

df.residual

Degrees of freedom residuals.

residuals

Residuals.

fitted.values

Fitted values.

order

Vector containing (r,s,q), i.e. causal order r, noncausal order s, number of exogenous regressors q.

Examples

# NOT RUN {
data <- sim.marx(c('t',1,1), c('t',1,1),100,0.5,0.4,0.3)
object <- mixed(data$y, data$x, 1, 1)
class(object) <- "mixed"
summary(object)
# }