sim_monthly: Simulate a monthly seasonal series

Description

Simulate a monthly seasonal series

Usage

sim_monthly(
  N,
  sd = 5,
  change_sd = sd/10,
  beta_1 = 0.6,
  beta_tau = 0.4,
  moving = TRUE,
  model = list(order = c(3, 1, 1), ma = 0.5, ar = c(0.2, -0.4, 0.1)),
  start = c(2010, 1),
  multiplicative = TRUE,
  extra_smooth = FALSE
)

Value

Multiple simulated monthly time series of class xts including:

original: The original series
seas_adj: The original series without seasonal effects
sfac: The seasonal effect

Arguments

N: Length in years
sd: Standard deviation for all seasonal factors
change_sd: Standard deviation of shock to seasonal factor
beta_1: Persistance wrt to previous period of the seasonal change
beta_tau: Persistence wrt to one year/cycle of the seasonal change
moving: Is the seasonal pattern allowed to change over time
model: Model for non-seasonal time series. A list.
start: Start date of output time series
multiplicative: Boolean. Should multiplicative seasonal factors be simulated
extra_smooth: Boolean. Should the seasonal factors be smooth 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 for the initialization of the seasonal factor 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.

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

x=sim_monthly(5, multiplicative=TRUE)
ts.plot(x[,1])

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