tseg: Simulate Some Time Series Models of Interest

In artfima: ARTFIMA Model Estimation

Description

Time series models are simulated based on some familar characteristics described in Details.

Usage

tseg(n, which = c("BJAR2", "BJAR1", "BJAR3", "PWAR4", "BJARMA11", "MHAR9", 
	"NileMin", "SB32"))

Arguments

n	length of series
which	which model

Details

BJAR1 is the AR(1) model fit to the sunspot series in BJR BJAR2 is the AR(2) model fit to the sunspot series in BJR BJAR3 is the AR(3) model fit to the sunspot series in BJR BJAR2 is the AR(2) model fit to the sunspot series in BJR PWAR4 is the AR(4) model, PW, BJARMA11 is the ARMA(1,1) model fit to Series A in BJR MHAR9 is the AR(9) model fit to the sunspot series in MHL NileMin is ARFIMA(0,0,0), d=0.39 SB32 is ARTFIMA(0,0,0), d=5/8, lambda=0.045

Value

vector of time series values

Author(s)

A. I. McLeod

References

BJR) Box, Jenkins and Reinsel (2005), Table 7.11 PW) Percival and Walden, 1990, p.45 MHL) McLeod, Hipel and Lennox, 1978, p.581

See Also

artsim

Examples

z <- tseg(5000, "MHAR9")
arima(z, order=c(9,0,0), fixed=c(NA,NA,0,0,0,0,0,0,NA,NA), transform.pars=FALSE)

Example output

Call:
arima(x = z, order = c(9, 0, 0), transform.pars = FALSE, fixed = c(NA, NA, 0, 
    0, 0, 0, 0, 0, NA, NA))

Coefficients:
         ar1      ar2  ar3  ar4  ar5  ar6  ar7  ar8     ar9  intercept
      1.2195  -0.5102    0    0    0    0    0    0  0.2036    10.8623
s.e.  0.0108   0.0106    0    0    0    0    0    0  0.0060     0.3302

sigma^2 estimated as 4.173:  log likelihood = -10668.59,  aic = 21347.18

artfima documentation built on May 2, 2019, 1:27 p.m.