sim_sfac: Simulate a seasonal factor

Description

Simulate a seasonal factor

Usage

sim_sfac(
  n,
  freq = 12,
  sd = 1,
  change_sd = sd/10,
  moving = TRUE,
  beta_1 = 0.6,
  beta_tau = 0.4,
  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 = 7,
  extra_smooth = FALSE
)

Value

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

Arguments

n: Number of observations
freq: Frequency of the time series
sd: Standard deviation of the seasonal factor
change_sd: Standard deviation of shock to seasonal factor
moving: Is the seasonal pattern allowed to change over time
beta_1: Persistence wrt to previous period of the seasonal change
beta_tau: Persistence wrt to one year/cycle 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 smoothed on a period-by-period basis

Author

Daniel Ollech

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 in line with the chosen model.

References

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

Examples

Run this code

ts.plot(sim_sfac(60))

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