mixed: The MARX estimation function

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

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)

## Default S3 method:
mixed(y, x, p_C, p_NC)

## S3 method for class 'mixed'
print(x, ...)

## S3 method for class '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

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)

Example output

There were 50 or more warnings (use warnings() to see the first 50)
              Length Class  Mode   
coefficients   4     -none- numeric
se             4     -none- numeric
df.residual    1     -none- numeric
residuals     98     -none- numeric
fitted.values 98     -none- numeric
call           5     -none- call   

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