R/fix.pacf.test.R

In Sie2nts: Sieve Methods for Non-Stationary Time Series

# testing for pacf


source(paste(getwd(), "/R/Sie2nts.Legen.v1.R", sep = ""))
source(paste(getwd(), "/R/Sie2nts.Cheby.v1.R", sep = ""))
source(paste(getwd(), "/R/Sie2nts.Four.v1.R", sep = ""))
source(paste(getwd(), "/R/Sie2nts.Csp.v1.R", sep = ""))
source(paste(getwd(), "/R/Sie2nts.db1-20.v1.R", sep = ""))


#' Testing Lag of Auto-Regressive (AR) Model
#' @description fix.pacf.test() generates a test of lags for AR Approximations.
#' @param ts ts is the data set which is a time series data typically
#' @param c c indicates the number of basis used to estimate (For wavelet, the number of basis is 2^c. If
#'     Cspli is chosen, the real number of basis is c-2+or)
#' @param type type indicates which type of basis is used. There are 31 types in this package
#' @param or or indicates the order of spline and only used in Cspli type, default is 4 which indicates cubic spline
#' @param lag lag determine the lag of AR Approximations.The default is 3
#' @param b the largest lag for auto-regressive model, the default value is 8, this parameter must be
#'     larger than lag
#' @param B.s the number of statistics used in multiplier bootstrap, the default value is 1000
#' @param m the number of window size used in multiplier bootstrap, the default value is 0 which uses the
#'     minimum volatility method to determine the number
#' @return It returns a list contains p value for each lag
#' @export



fix.pacf.test = function(ts, c, type, or=4, lag = 3, b = 8, B.s  = 1000, m = 0){
  wavelet_basis = c("db1", "db2", "db3", "db4", "db5",
                    "db6", "db7", "db8", "db9", "db10",
                    "db11", "db12", "db13", "db14", "db15",
                    "db16", "db17", "db18", "db19", "db20",
                    "cf1", "cf2", "cf3", "cf4", "cf5"
  )

  if(type == "Legen"){
    return(fit.testing.b.legen(ts, c, lag, b = b, B.s  = B.s, m = m))
  } else if (type == "Cheby"){
    return(fit.testing.b.cheby(ts, c, lag, b = b, B.s  = B.s, m = m))
  } else if (type %in% c("tri", "cos", "sin")){
    return(fit.testing.b.four(ts,c,lag, ops = type, b = b, B.s  = B.s, m = m))
  } else if (type == "Cspli"){
    return(fit.testing.b.cspline(ts, c,or=or, lag, b = b, B.s  = B.s, m = m))
  } else if (type %in% wavelet_basis){
    return(fit.testing.b.wavelet(ts,c,lag,  b = b, ops = type,  B.s  = B.s, m = m))
  } else{
    return(stop("Invalid option!"))
  }
}