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rmgarch (version 1.2-9)

varxfit: VARX Fit/Filter/Forecast/Simulation Functions

Description

Vector Autoregressive (VAR) with Constant and Optional Exogenous Regressors (X) Fit, Filter, Forecast and Simulation functions for use with multivariate GARCH models.

Usage

varxfit(X, p, constant = TRUE, exogen = NULL, robust = FALSE, gamma = 0.25, 
delta = 0.01, nc = 10, ns = 500, postpad = c("none", "constant", "zero", "NA"), 
cluster = NULL)
varxfilter(X, p, Bcoef, exogen = NULL, postpad = c("none", "constant", "zero", "NA"))
varxforecast(X, Bcoef, p, out.sample, n.ahead, n.roll, mregfor)
varxsim (X, Bcoef, p, n.sim, n.start, prereturns, resids, mexsimdata)

Arguments

X
A multivariate data matrix.
p
The number of autoregressive lags.
constant
Whether to include a constant.
exogen
An optional matrix of exogenous regressors with as many rows as X, and appropriately lagged.
Bcoef
A matrix of coefficients for the varxfilter function.
robust
Whether to use the robust version of VAR based on the multivariate Least Trimmed Squares Estimator described in Croux and Joossens (2008).
gamma
Proportion to trim in the robust method.
delta
The critical value for Reweighted estimator for the robust method.
ns
The number of subsets to use for the robust method.
nc
The number of C-steps to use for the robust method.
postpad
(defaults to none) Whether to postpad the fitted/filtered values (and hence calculation of residuals) with the estimated constant, zeros or NA's, thus returning matrices of the same size as the input data (rather than input data
cluster
A cluster object created by calling makeCluster from the parallel package. If it is not NULL, then this will be used for parallel estimation in the case of the robust VAR version (remember to stop the cluster on completion).
out.sample
The number of points kept for out of sample rolling forecast.
n.ahead
The forecast horizon.
n.roll
In combination with out.sample, determines the number of times to roll forward the n.ahead forecast using data left out of sample.
mregfor
Matrix of external regressor forecasts (with appropriate lag structure).
n.sim
Simulation horizon.
n.start
Simulation burn-in sample.
prereturns
Optionally supplied pre-return matrix with p lags to initialize simulation.
resids
Matrix of randomly generated residuals of size n.sim+n.start.
mexsimdata
Matrix of external regressor pre-generated random values to use in the simulation (if NULL then assumed zero).

Value

  • A list with the following items:
  • Bcoef[varxfit, varxfilter] The coefficient matrix with rows equal to number of assets, and columns equal to number of assets x number of lags plus 1 (constant) plus number of exogenous regressors.
  • xfitted[varxfit, varxfilter] The fitted/filtered series (conditional mean series).
  • xresiduals[varxfit, varxfilter] The residuals.
  • Bcov[varxfit] The covariance matrix of the coefficients.
  • se[varxfit] The standard error of the coefficients.
  • tstat[varxfit] The t-stat of the s.e.
  • pstat[varxfit] The p-values of the s.e.
  • lag[varxfit, varxfilter] The number of autoregressive lags.
  • mxn[varxfit] The number of exogenous regressors .
  • meansim[varxsim] The simulated conditional mean.
  • The varxsim returns an n.sim x n.asset matrix of the simulated conditional means, while the varxforecast returns an n.ahead x n.assets x (n.roll+1) array of the forecast conditional means.

Details

This are convenience functions to be optionally used when using the multivariate GARCH methods.

References

Lutkepohl, H. 2005, New introduction to multiple time series analysis, Springer. Croux, C. and Joossens, K. 2008, Robust estimation of the vector autoregressive model by a least trimmed squares procedure, COMPSTAT, 489--501.