sim_monthly: Simulate a monthly seasonal series

In tssim: Simulation of Daily and Monthly Time Series

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
)

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

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.

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

Author(s)

Daniel Ollech

References

Ollech, D. (2021). Seasonal adjustment of daily time series. Journal of Time Series Econometrics. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1515/jtse-2020-0028")}

Examples

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

tssim documentation built on April 3, 2025, 7:39 p.m.