TVAR: Multivariate Threshold Vector Autoregressive model

In tsDyn: Nonlinear Time Series Models with Regime Switching

Multivariate Threshold Vector Autoregressive model

Description

Estimate a multivariate Threshold VAR (TVAR), either using lags as transition variable (default), or specifying an external variable.

Usage

TVAR(
  data,
  lag,
  include = c("const", "trend", "none", "both"),
  model = c("TAR", "MTAR"),
  commonInter = FALSE,
  nthresh = 1,
  thDelay = 1,
  mTh = 1,
  thVar,
  trim = 0.1,
  ngrid,
  gamma = NULL,
  around,
  plot = FALSE,
  dummyToBothRegimes = TRUE,
  trace = TRUE,
  trick = "for",
  max.iter = 2
)

Arguments

data	time series
lag	Number of lags to include in each regime
include	Type of deterministic regressors to include
model	Whether the transition variable is taken in levels (TAR) or difference (MTAR)
commonInter	Whether the deterministic regressors are regime specific (commonInter=FALSE) or not.
nthresh	Number of thresholds
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
thVar	Optional. External transition variable.
trim	trimming parameter indicating the minimal percentage of observations in each regime
ngrid	number of elements of the grid, especially for nthresh=3
gamma	prespecified threshold values
around	The grid search is restricted to ngrid values around this point. Especially useful for nthresh=3.
plot	Whether a plot showing the results of the grid search should be printed
dummyToBothRegimes	Whether the dummy in the one threshold model is applied to each regime or not.
trace	should additional infos be printed out?
trick	type of R function called: for or mapply
max.iter	Number of iterations for the algorithm

Details

For fixed th and threshold variable, the model is linear, so estimation can be done directly by CLS (Conditional Least Squares). The search of the parameters values is made upon a grid of potential values. So it is pretty slow.

nthresh=1: estimation of one threshold model (two regimes) upon a grid of ngrid values (default to ALL) possible thresholds and delays values.

nthresh=2: estimation of two thresholds model (three regimes) Conditional on the threshold found in model where nthresh=1, the second threshold is searched. When both are found, a second grid search is made with 30 values around each threshold.

nthresh=3: DOES NOT estimate a 3 thresholds model, but a 2 thresholds model with a whole grid over the thresholds parameters (so is really slow) with a given delay, is there rather to check the consistency of the method nthresh=2

Value

An object of class TVAR, with standard methods.

Author(s)

Matthieu Stigler

References

Lo and Zivot (2001) "Threshold Cointegration and Nonlinear Adjustment to the Law of One Price," Macroeconomic Dynamics, Cambridge University Press, vol. 5(4), pages 533-76, September.

See Also

lineVar for the linear VAR/VECM, TVAR.LRtest to test for TVAR, TVAR.sim to simulate/bootstrap a TVAR.

Examples

data(zeroyld)

tv <- TVAR(zeroyld, lag=2, nthresh=2, thDelay=1, trim=0.1, mTh=1, plot=FALSE)

print(tv)
summary(tv)

# a few useful methods:
plot(tv)
predict(tv)
c(AIC(tv), BIC(tv), logLik(tv))

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