sim_sfac: Simulate a seasonal factor

In tssim: Simulation of Daily and Monthly Time Series

Description

Simulate a seasonal factor

Usage

sim_sfac(
  n,
  freq = 12,
  sd = 1,
  change_sd = 0.02,
  beta_1 = 0.9,
  beta_tau = 0,
  start = c(2020, 1),
  multiplicative = TRUE,
  ar = NULL,
  ma = NULL,
  model = c(1, 1, 1),
  sc_model = list(order = c(1, 1, 1), ar = 0.65, ma = 0.25),
  smooth = TRUE,
  burnin = 3,
  extra_smooth = FALSE
)

Arguments

n	Number of observations
freq	Frequency of the time series
sd	Standard deviation of the seasonal factor
change_sd	Standard deviation of simulation change to seasonal factor
beta_1	Persistance wrt to previous period of the seasonal change
beta_tau	Persistance wrt to one year/cycle before of the seasonal change
start	Start date of output time series
multiplicative	Boolean. Should multiplicative seasonal factors be simulated
ar	AR parameter
ma	MA parameter
model	Model for initial seasonal factor
sc_model	Model for the seasonal change
smooth	Boolean. Should initial seasonal factor be smoothed
burnin	(burnin*n-n) is the burn-in period
extra_smooth	Boolean. Should the seasonal factor be smooth on a period-by-period basis

Details

Standard deviation of the seasonal factor is in percent if a multiplicative time series model is assumed. Otherwise it is in unitless. Using a non-seasonal ARIMA model does not impact the seasonality of the time series. It can just make it easier for human eyes to grasp the seasonal nature of the series. The definition of the ar and ma parameter needs to be inline with the chosen model.

Value

The function returns a time series of class ts containing a seasonal or periodic effect.

Author(s)

Daniel Ollech

References

Ollech, D. (2021). Seasonal adjustment of daily time series. Journal of Time Series Econometrics. doi: 10.1515/jtse-2020-0028

Examples

ts.plot(sim_sfac(60))

tssim documentation built on June 25, 2021, 5:06 p.m.