sim.urarima: Generate Unit-root ARIMA Possibly Seasonal Time Series

In SLBDD: Statistical Learning for Big Dependent Data

Generate Unit-root ARIMA Possibly Seasonal Time Series

Description

Generate Unit-root ARIMA, possibly, seasonal time series.

Usage

sim.urarima(
  T = 300,
  ar = c(0.5),
  ma = c(-0.5),
  d = 1,
  sar = NULL,
  sma = NULL,
  D = 0,
  period = 12,
  ini = 200,
  df = 50
)

Arguments

T	Number of observations.
ar	Vector with the autoregressive coefficients. Default value is 0.5.
ma	Vector with the moving average coefficients. Default value is -0.5.
d	Order of first-differencing. Default value is 1.
sar	Seasonal autoregressive coefficients. Default is NULL.
sma	Seasonal moving average coefficients. Default is NULL.
D	Order of seasonal differencing. Default value is 0.
period	Seasonal period. Default value is 12.
ini	Length of ‘burn-in’ period. Default value is 200.
df	If df ≥q 50 random generation for the Normal distribution, if df < 50 random generation for the t distribution with df degrees of freedom. Default value is 50.

Value

A time series vector.

Examples

x <- sim.urarima()

SLBDD documentation built on April 27, 2022, 5:08 p.m.