coef.arx: Extraction functions for 'arx' objects

In gets: General-to-Specific (GETS) Modelling and Indicator Saturation Methods

Extraction functions for 'arx' objects

Description

Extraction functions for objects of class 'arx'

Usage

  ## S3 method for class 'arx'
coef(object, spec=NULL, ...)
  ## S3 method for class 'arx'
fitted(object, spec=NULL, ...)
  ## S3 method for class 'arx'
logLik(object, ...)
  ## S3 method for class 'arx'
model.matrix(object, spec=c("mean","variance"), response=FALSE, as.zoo=TRUE, ...)
  ## S3 method for class 'arx'
nobs(object, spec=NULL, ...)
  ## S3 method for class 'arx'
plot(x, spec=NULL, col=c("red","blue"),
    lty=c("solid","solid"), lwd=c(1,1), ...)
  ## S3 method for class 'arx'
print(x, signif.stars=TRUE, ...)
  ## S3 method for class 'arx'
residuals(object, std=FALSE, ...)
  ## S3 method for class 'arx'
sigma(object, ...)
  ## S3 method for class 'arx'
summary(object, ...)
  ## S3 method for class 'arx'
vcov(object, spec=NULL, ...)

Arguments

object	an object of class 'arx'
x	an object of class 'arx'
spec	NULL, "mean", "variance" or, in some instances, "both". When NULL is a valid value, then it is automatically determined whether information pertaining to the mean or variance specification should be returned
response	logical. If TRUE, then the response is included in the first column
as.zoo	logical. If TRUE (default), then the returned matrix is of class zoo
signif.stars	logical. If TRUE, then p-values are additionally encoded visually, see printCoefmat
std	logical. If FALSE (default), then the mean residuals are returned. If TRUE, then the standardised residuals are returned
col	colours of actual (default=blue) and fitted (default=red) lines
lty	types of actual (default=solid) and fitted (default=solid) lines
lwd	widths of actual (default=1) and fitted (default=1) lines
...	additional arguments

Value

coef:	a numeric vector containing parameter estimates
fitted:	a zoo object with fitted values
logLik:	log-likelihood (normal density)
model.matrix:	a matrix with the regressors and, optionally, the response
nobs:	the number of observations
plot:	a plot of the fitted values and the residuals
print:	a print of the estimation results
residuals:	a zoo object with the residuals
sigma:	the regression standard error ('SE of regression')
summary:	a print of the items in the arx object
vcov:	variance-covariance matrix

Author(s)

Felix Pretis, https://felixpretis.climateeconometrics.org/
James Reade, https://sites.google.com/site/jjamesreade/
Moritz Schwarz, https://www.inet.ox.ac.uk/people/moritz-schwarz
Genaro Sucarrat, http://www.sucarrat.net/

See Also

arx

Examples

##simulate from an AR(1):
set.seed(123)
y <- arima.sim(list(ar=0.4), 40)

##simulate four independent Gaussian regressors:
xregs <- matrix(rnorm(4*40), 40, 4)

##estimate an 'arx' model: An AR(2) with intercept and four conditioning
##regressors in the mean, and log-ARCH(3) in the variance:
mymod <- arx(y, mc=TRUE, ar=1:2, mxreg=xregs, arch=1:3)

##print results:
print(mymod)

##plot the fitted vs. actual values, and the residuals:
plot(mymod)

##print the entries of object 'mymod':
summary(mymod)

##extract coefficient estimates (automatically determined):
coef(mymod)

##extract mean coefficients only:
coef(mymod, spec="mean")

##extract log-variance coefficients only:
coef(mymod, spec="variance")

##extract all coefficient estimates:
coef(mymod, spec="both")

##extract regression standard error:
sigma(mymod)

##extract log-likelihood:
logLik(mymod)

##extract variance-covariance matrix of mean equation:
vcov(mymod)

##extract variance-covariance matrix of log-variance equation:
vcov(mymod, spec="variance")

##extract and plot the fitted mean values (automatically determined):
mfit <- fitted(mymod)
plot(mfit)

##extract and plot the fitted variance values:
vfit <- fitted(mymod, spec="variance")
plot(vfit)

##extract and plot both the fitted mean and variance values:
vfit <- fitted(mymod, spec="both")
plot(vfit)

##extract and plot the fitted mean values:
vfit <- fitted(mymod, spec="mean")
plot(vfit)

##extract and plot residuals:
epshat <- residuals(mymod)
plot(epshat)

##extract and plot standardised residuals:
zhat <- residuals(mymod, std=TRUE)
plot(zhat)

gets documentation built on Sept. 11, 2024, 9:03 p.m.