autostsm (version 1.1)

stsm_ssm: State space model

Description

Creates a state space model in list form yt = H*B + e_t B = F*B_t-1 + u_t

Usage

stsm_ssm(
  par = NULL,
  yt = NULL,
  freq = NULL,
  decomp = NULL,
  trend = 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 (monthly), 365.25/7 (weekly), 365.25 (daily))

decomp

Decomposition model ("tend-cycle-seasonal", "trend-seasonal", "trend-cycle", "trend-noise")

trend

Trend specification ("random-walk", "random-walk-drift", "double-random-walk", "random-walk2"). The default is NULL which will choose the best of all specifications based on the maximum likielhood. "random-walk" is the random walk trend. "random-walk-drift" is the random walk with constant drift trend. "double-random-walk" is the random walk with random walk drift trend. "random-walk2" is a 2nd order random walk trend as in the Hodrick-Prescott filter.

init

Initial state values for the Kalman filter

model

a stsm_estimate model object

Value

List of space space matrices

Examples

# NOT RUN {
#GDP Not seasonally adjusted
library(autostsm)
data("NA000334Q", package = "autostsm") #From FRED
NA000334Q = data.table(NA000334Q, keep.rownames = TRUE)
colnames(NA000334Q) = c("date", "y")
NA000334Q[, "date" := as.Date(date)]
NA000334Q[, "y" := as.numeric(y)]
NA000334Q = NA000334Q[date >= "1990-01-01", ]
stsm = stsm_estimate(NA000334Q)
ssm = stsm_ssm(model = stsm)
# }