TVAR.sim: Simulation of a multivariate Threshold Autoregressive model...

In tsDyn: Nonlinear Time Series Models with Regime Switching

Simulation of a multivariate Threshold Autoregressive model (TVAR)

Description

Simulate a multivariate Threshold VAR (TVAR)

Usage

TVAR.sim(
  B,
  Thresh,
  nthresh = 1,
  n = 200,
  lag = 1,
  include = c("const", "trend", "none", "both"),
  thDelay = 1,
  mTh = 1,
  starting = NULL,
  innov = rmnorm(n, varcov = varcov),
  varcov = diag(1, nrow(B)),
  show.parMat = FALSE,
  ...
)

TVAR.boot(
  TVARobject,
  innov,
  seed,
  boot.scheme = c("resample", "wild1", "wild2", "check"),
  ...
)

Arguments

B	Matrix of coefficients to simulate
Thresh	The threshold value(s). Vector of length nthresh
nthresh	number of threshold (see details)
n	Number of observations to create when type="simul"
lag	Number of lags to include in each regime
include	Type of deterministic regressors to include. NOT WORKING PROPERLY CURRENTLY if not const
thDelay	'time delay' for the threshold variable (as multiple of embedding time delay d) PLEASE NOTE that the notation is currently different to univariate models in tsDyn. The left side variable is taken at time t, and not t+1 as in univariate cases.
mTh	combination of variables with same lag order for the transition variable. Either a single value (indicating which variable to take) or a combination
starting	Starting values (matrix of dimension lag x k). If not given, set to zero.
innov	Innovations used for simulation. Should be matrix of dim n x k. By default multivariate normal. For the bootstrap case TVAR.boot, residuals are resampled if argument is missing.
varcov	Variance-covariance matrix for the innovations. By default identity matrix.
show.parMat	Logical. Should the parameter matrix be shown? Useful to understand how to give right input
...	Further arguments passed to the underlying (un-exported) TVAR.gen function
TVARobject	Object of class TVAR generated by function TVAR
seed	Optional. Seed for the random resampling function.
boot.scheme	The bootstrap scheme.

Details

This function offers the possibility to generate series following a TVAR.

By giving a matrix of coefficients, on can only simulate a VAR (nthresh=0) or TVAR (nthresh=1 or 2). One can have a specification with constant (default), trend, both or none (see arg include). Order in parameters is include/lags (VECM) and include/lags/include/lags for TVECM, hence, a matrix for a TVECM with 3 regimes, a const and a 2 lags would have 2 lines and 2*(1+4) columns. The innovations can be given by the user (a matrix of dim nxk, here n does not include the starting values!), by default it uses a multivariate normal distribution, with covariance matrix specified by varcov. The starting values (of dim lags x k) can be given. The user should take care for their choice, since it is not sure that the simulated values will cross the threshold even once.

The matrix ‘B’ has to be in the form: constant, trend, lags, then repeated if many regimes. In case of uncertainty, using who.parMat=TRUE will print the matrix as interpreted by the function, helping the user to feed the right input.

For the bootstrap, the function resamples data from a given TVAR model generated by TVAR, returning the resampled data. A residual recursive bootstrap is used, where one uses either a simple resampling, or the Wild bootstrap, either with a normal distribution (wild1) or inverting the sign randomly (wild2)

Value

A matrix with the simulated/bootstrapped series.

Author(s)

Matthieu Stigler

See Also

TVAR to estimate the TVAR. Similar TVECM.sim and TVECM.boot for TVECM, VAR.sim and VAR.boot for VAR models estimated with lineVar.

Examples

## TVAR.sim: Simulation of a TVAR with 1 threshold
B <- rbind(c(0.11928245, 1.00880447, -0.009974585, -0.089316, 0.95425564, 0.02592617),
        c(0.25283578, 0.09182279,  0.914763741, -0.0530613, 0.02248586, 0.94309347))
colnames(B) <- paste(rep(c("Const", "Lag_1_var1", "Lag_1_var2"), 2), c("Low", "High"), sep="_")
sim <- TVAR.sim(B=B,nthresh=1,n=500, mTh=1, Thresh=5, starting=matrix(c(5.2, 5.5), nrow=1))

#estimate the new serie
TVAR(sim, lag=1, dummyToBothRegimes=TRUE)






## TVAR.boot: Bootstrap a TVAR with two threshold (three regimes)
data(zeroyld)
serie <- zeroyld
mod <- TVAR(data=serie,lag=1, nthresh=1)
TVAR.boot(mod)

tsDyn documentation built on Oct. 31, 2024, 5:08 p.m.