boiwsa: Seasonal adjustment of weekly data

In boiwsa: Seasonal Adjustment of Weekly Data

Seasonal adjustment of weekly data

Description

Performs seasonal adjustment of weekly data. For more details on the usage of this function see the paper or the examples on Github.

Usage

boiwsa(
  x,
  dates,
  r = 0.8,
  auto.ao.search = TRUE,
  out.threshold = 3.8,
  ao.list = NULL,
  my.k_l = NULL,
  H = NULL,
  ic = "aicc",
  method = "additive"
)

Arguments

x	Input time series as a numeric vector
dates	a vector of class "Date", containing the data dates
r	Defines the rate of decay of the weights. Should be between zero and one. By default is set to 0.8.
auto.ao.search	Boolean. Search for additive outliers
out.threshold	t-stat threshold in outlier search. By default is 3.8
ao.list	Vector with user specified additive outliers in a date format
my.k_l	Numeric vector defining the number of yearly and monthly trigonometric variables. If NULL, is found automatically using the information criteria. The search range is 0:36 and 0:12 with the step size of 6 for the yearly and monthly variables, respectively.
H	Matrix with holiday- and trading day factors
ic	Information criterion used in the automatic search for the number of trigonometric regressors. There are thee options: aic, aicc and bic. By default uses aicc
method	Decomposition type: additive or multiplicative

Value

sa Seasonally adjusted series

my.k_l Number of trigonometric variables used to model the seasonal pattern

sf Estimated seasonal effects

hol.factors Estimated holiday effects

out.factors Estimated outlier effects

beta Regression coefficients for the last year

m lm object. Unweighted OLS regression on the full sample

Author(s)

Tim Ginker

Examples

 # Not run
 # Seasonal adjustment of weekly US gasoline production
 
 data("gasoline.data")
 res=boiwsa(x=gasoline.data$y,dates=gasoline.data$date)

boiwsa documentation built on April 4, 2025, 1:57 a.m.