MARX (version 0.2)

marx.t: The estimation of the MARX model by t-MLE function

Description

This function allows you to estimate the MARX model by t-MLE.

Usage

marx.t(y, x, p_C, p_NC, params0)

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.

p_NC

Number of leads.

params0

Starting values for the parameters to be estimated (both model and distributional parameters).

Value

coef.c

Estimated causal coefficients.

coef.nc

Estimated noncausal coefficients.

coef.exo

Estimated exogenous coefficients.

coef.int

Estimated intercept.

scale

Estimated scale parameter.

df

Estimated degrees of freedom.

residuals

Residuals.

se.dist

Standard errors of the distributional parameters.

Examples

# NOT RUN {
data <- sim.marx(c('t',3,1),c('t',3,1),100,0.5,0.4,0.3)
marx.t(data$y,data$x,1,1)
# }