inference: Asymptotic inference for the MARX function

In MARX: Simulation, Estimation, Model Selection and Forecasting for MARX Models

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.

Author(s)

Sean Telg

Examples

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)

Example output

There were 16 warnings (use warnings() to see them)
$CI.c
          [,1]      [,2]
[1,] 0.4864931 0.5393546

$CI.nc
          [,1]      [,2]
[1,] 0.3163578 0.3745817

$CI.exo
         [,1]      [,2]
[1,] 0.120118 0.2619454

$CI.int
[1] -0.3280816  0.1594443

$se.c
           [,1]
[1,] 0.01348532

$se.nc
           [,1]
[1,] 0.01485332

$se.exo
           [,1]
[1,] 0.03618111

$se.int
[1] 0.1243712

MARX documentation built on May 2, 2019, 3:42 a.m.