State space model
Creates a state space model in list form yt = H*B + e_t B = F*B_t-1 + u_t
stsm_ssm( par = NULL, yt = NULL, decomp = NULL, trend = NULL, init = NULL, model = NULL )
par |
Vector of named parameter values, includes the harmonics |
yt |
Univariate time series of data values |
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 |
List of space space matrices
## 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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.