MARX (version 0.2)

inference: Asymptotic inference for the MARX function

Description

This function allows you to calculate standard errors and confidence intervals for parameters of the MARX model.

Usage

inference(y, x, B_C, B_NC, B_x, IC, sig, df, sig_level)

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.

B_C

Estimated causal parameters of the MARX.

B_NC

Estimated noncausal parameters of the MARX.

B_x

Estimated parameters of the exogenous variables in the MARX.

IC

Estimated intercept.

sig

Estimated scale parameter of the assumed underlying Student-t distribution of the residuals.

df

Estimated degrees of freedom of the assumed underlying Student-t distribution of the residuals.

sig_level

Significance level for the construction of inference.

Value

CI.c

Confidence intervals for causal parameters.

CI.nc

Confidence intervals for noncausal parameters.

CI.exo

Confidence intervals for exogenous parameters.

CI.int

Confidence interval for intercept.

se.c

Standard errors of causal parameters.

se.nc

Standard errors of noncausal parameters.

se.exo

Standard errors of exogenous parameters.

se.int

Standard error of intercept.

Examples

# NOT RUN {
data <- sim.marx(c('t',1,1), c('t',1,1),100,0.5,0.4,0.3)
y <- data$y
x <- data$x
res <- marx.t(y,x,1,1)
inference(y,x,res$coef.c,res$coef.nc,res$coef.exo,res$coef.int,res$scale,res$df,0.05)
# }