tcs_ssm: State space model Creates a state space model in list form yt...

In opendoor-labs/TCSDecomp: Structrual time series decomposition using the Kalman filter

Description

State space model Creates a state space model in list form yt = Ht * Bt + e_t Bt = Ft * B_t-1 + u_t

Usage

tcs_ssm(
  par = NULL,
  yt = NULL,
  freq = NULL,
  decomp = NULL,
  trend_spec = NULL,
  init = NULL,
  model = NULL
)

Arguments

par	Vector of named parameter values, includes the harmonics
yt	Univariate time series of data values
freq	Frequency of the data (1 (yearly), 4 (quarterly), 12 (montly), 52 (weekly), 365 (daily))
decomp	Decomposition model ("tend-cycle-seasonal", "trend-seasonal", "trend-cycle", "trend-noise")
trend_spec	Trend specification (NULL, "rw", "rwd", "2rw"). The default is NULL which will choose the best of all specifications based on the maximum likielhood. "rw" is the random walk trend. "rwd" is the random walk with random walk drift trend. "2rw" is a 2nd order random walk trend.
init	Initial state values for the Kalman filter
model	a tcs_decomp_estim model object

Value

List of space space matrices

Author(s)

Alex Hubbard ([e-mail])

Examples

tcs_ssm(par, y, freq, decomp, trend_spec, init)

opendoor-labs/TCSDecomp documentation built on April 21, 2020, 3:15 a.m.