fix.fit {Sie2nts}

Estimate the Coefficients of Auto-Regressive (AR) Model by User Specifying

Description

fix.fit() estimates the coefficients of AR model by sieve methods with user specifying.

Usage

fix.fit(ts, c, b, type, or = 4, m = 500)

Arguments

ts

ts is the data set which is a time series data typically

c

c indicates the number of basis used to estimate (For wavelet, the real number of basis is 2^c. For Cubic Spline, the real number of basis is c-2+or)

b

b is the lag for auto-regressive model

type

type indicates which type of basis is used. There are 31 types in this package

or

indicates the order of spline and only used in Cspli type, default is 4 which indicates cubic spline

m

m indicates the number of points of coefficients to estimate

Value

A list contains 3 objects, the first is a matrix which contains estimates for each basis used in OLS, the second is a list contains estimates for coefficients in AR model and the last is a vector contains residuals

Examples

set.seed(137)
time.series = c()
n = 1024
v = 25
w = rnorm(n, 0, 1) / v
x_ini = runif(1,0,1)
for(i in 1:n){
  if(i == 1){
    time.series[i] = 0.2 + 0.6*cos(2*pi*(i/n))*x_ini  + w[i] #
  } else{
    time.series[i] = 0.2 + 0.6*cos(2*pi*(i/n))*time.series[i-1] + w[i]
  }
}
res = fix.fit(time.series, c=5, b=1, type = "Legen")
cat(res$ols.coef)
plot.ts(res$ts.coef[[1]])
plot.ts(res$Residuals)