stsm_filter {autostsm}R Documentation

Kalman Filter

Description

Kalman filter an estimated model from stsm_estimate output. This is a wrapper to stsm_forecast with n.ahead = 0.

Usage

stsm_filter(
  model,
  y,
  freq = NULL,
  exo_obs = NULL,
  exo_state = NULL,
  ci = 0.8,
  plot = FALSE,
  plot.decomp = FALSE,
  smooth = TRUE
)

Arguments

model

Structural time series model estimated using stsm_estimate.

y

Univariate time series of data values. May also be a 2 column data frame containing a date column.

freq

Frequency of the data (1 (yearly), 4 (quarterly), 12 (monthly), 365.25/7 (weekly), 365.25 (daily)), default is NULL and will be automatically detected

exo_obs

Matrix of exogenous variables to be used in the observation equation.

exo_state

Matrix of exogenous variables to be used in the state matrix.

ci

Confidence interval, value between 0 and 1 exclusive.

plot

Logical, whether to plot everything

plot.decomp

Logical, whether to plot the filtered historical data

smooth

Whether or not to use the Kalman smoother

Value

data table (or list of data tables) containing the filtered and/or smoothed series.

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)
fc = stsm_filter(stsm, y = NA000334Q, plot = TRUE)

## End(Not run)

[Package autostsm version 3.0.1 Index]