startingVLSTAR {starvars}

Starting parameters for a VLSTAR model

Description

This function allows the user to obtain the set of starting values of Gamma and C for the convergence algorithm via searching grid.

Usage

startingVLSTAR(
  y,
  exo = NULL,
  p = 1,
  m = 2,
  st = NULL,
  constant = TRUE,
  n.combi = NULL,
  ncores = 2,
  singlecgamma = FALSE
)

Arguments

y

data.frame or matrix of dependent variables of dimension (Txn)

exo

(optional) data.frame or matrix of exogenous variables of dimension (Txk)

p

lag order

m

number of regimes

st

single transition variable for all the equation of dimension (Tx1)

constant

TRUE or FALSE to include or not the constant

n.combi

Number of combination for the searching grid of Gamma and C

ncores

Number of cores used for parallel computation. Set to 2 by default

singlecgamma

TRUE or FALSE to use single gamma and c

Details

The searching grid algorithm allows for the optimal choice of the parameters \gamma and c by minimizing the sum of the Squared residuals for each possible combination.

The parameter c is initialized by using the mean of the dependent(s) variable, while \gamma is sampled between 0 and 100.

Value

An object of class startingVLSTAR.

Author(s)

Andrea Bucci

References

Anderson H.M. and Vahid F. (1998), Testing multiple equation systems for common nonlinear components. Journal of Econometrics. 84: 1-36

Bacon D.W. and Watts D.G. (1971), Estimating the transition between two intersecting straight lines. Biometrika. 58: 525-534

Terasvirta T. and Yang Y. (2014), Specification, Estimation and Evaluation of Vector Smooth Transition Autoregressive Models with Applications. CREATES Research Paper 2014-8

See Also

VLSTAR

Examples

data(Realized)
y <- Realized[-1,1:10]
y <- y[-nrow(y),]
st <- Realized[-nrow(Realized),1]
st <- st[-length(st)]
starting <- startingVLSTAR(y, p = 1, n.combi = 3,
                           singlecgamma = FALSE, st = st,
                           ncores = 1)