startingVLSTAR: Starting parameters for a VLSTAR model
In starvars: Vector Logistic Smooth Transition Models Estimation and Prediction

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`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

The searching grid algorithm allows for the optimal choice of the parameters γ 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 γ is sampled between 0 and 100.

An object of class startingVLSTAR.

Andrea Bucci

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

VLSTAR

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)