Description Usage Arguments Details Value Author(s) References See Also Examples
Estimation by OLS, twostep OLS if a variance specification is specified: In the first the mean specification (ARX) is estimated, whereas in the second step the logvariance specification (logARCHX) is estimated.
The ARX mean specification can contain an intercept, ARterms, lagged moving averages of the regressand and other conditioning covariates ('X'). The logvariance specification can contain logARCH terms, asymmetry or 'leverage' terms, log(EqWMA) where EqWMA is a lagged equally weighted moving average of past squared residuals (a volatility proxy) and other conditioning covariates ('X').
1 2 3 4 5 
y 
numeric vector, timeseries or 
mc 
logical. 
ar 
either 
ewma 
either 
mxreg 
either 
vc 
logical. 
arch 
either 
asym 
either 
log.ewma 
either 
vxreg 
either 
zero.adj 
numeric value between 0 and 1. The quantile adjustment for zero values. The default 0.1 means the zero residuals are replaced by the 10 percent quantile of the absolute residuals before taking the logarithm 
vc.adj 
logical. If 
vcov.type 
character vector, "ordinary" (default), "white" or "neweywest". If "ordinary", then the ordinary variancecovariance matrix is used for inference. If "white", then the White (1980) heteroscedasticityrobust matrix is used. If "neweywest", then the Newey and West (1987) heteroscedasticity and autocorrelationrobust matrix is used 
qstat.options 

user.estimator 

user.diagnostics 

tol 
numeric value ( 
LAPACK 
logical. If 
plot 

See Sucarrat and Escribano (2012)
A list of class 'arx'
Genaro Sucarrat, http://www.sucarrat.net/
Genaro Sucarrat and Alvaro Escribano (2012): 'Automated Financial Model Selection: GeneraltoSpecific Modelling of the Mean and Volatility Specifications', Oxford Bulletin of Economics and Statistics 74, Issue no. 5 (October), pp. 716735
Halbert White (1980): 'A HeteroskedasticityConsistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity', Econometrica 48, pp. 817838
Whitney K. Newey and Kenned D. West (1987): 'A Simple, Positive SemiDefinite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix', Econometrica 55, pp. 703708
Extraction functions (mostly S3 methods): coef.arx
, ES
, fitted.arx
, plot.arx
,
print.arx
, recursive
, residuals.arx
, sigma.arx
, rsquared
,
summary.arx
, VaR
and vcov.arx
Related functions: getsm
, getsv
, isat
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35  ##Simulate from an AR(1):
set.seed(123)
y < arima.sim(list(ar=0.4), 70)
##estimate an AR(2) with intercept:
arx(y, mc=TRUE, ar=1:2)
##Simulate four independent Gaussian regressors:
xregs < matrix(rnorm(4*70), 70, 4)
##estimate an AR(2) with intercept and four conditioning
##regressors in the mean:
arx(y, mc=TRUE, ar=1:2, mxreg=xregs)
##estimate a logvariance specification with a logARCH(4)
##structure:
arx(y, arch=1:4)
##estimate a logvariance specification with a logARCH(4)
##structure and an asymmetry/leverage term:
arx(y, arch=1:4, asym=1)
##estimate a logvariance specification with a logARCH(4)
##structure, an asymmetry or leverage term, a 10period log(EWMA) as
##volatility proxy, and the log of the squareds of the conditioning
##regressors in the logvariance specification:
arx(y, arch=1:4, asym=1, log.ewma=list(length=10), vxreg=log(xregs^2))
##estimate an AR(2) with intercept and four conditioning regressors
##in the mean, and a logvariance specification with a logARCH(4)
##structure, an asymmetry or leverage term, a 10period log(EWMA) as
##volatility proxy, and the log of the squareds of the conditioning
##regressors in the logvariance specification:
arx(y, mc=TRUE, ar=1:2, mxreg=xregs, arch=1:4, asym=1,
log.ewma=list(length=10), vxreg=log(xregs^2))

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