demo_variance_dynamics.R
In tsissm: Linear Innovations State Space Unobserved Components Model

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup, warning=FALSE,message=FALSE---------------------------------------
library(tsissm)
library(xts)
library(data.table)
library(tsaux)
library(knitr)
library(zoo)

## -----------------------------------------------------------------------------
data("spy", package = "tsissm")
y <- as.xts(spy)
xreg <- auto_regressors(y["2014/"], frequency = 1, lambda = 0, sampling = "days", method = "full",
                         check.rank = TRUE, discard.cval = 3.5, maxit.iloop = 10, maxit.oloop = 10, types = "LS")

exc <- which(xreg$xreg["2020-02-03"] == 1)
xreg$xreg <- xreg$xreg[,-exc]
xreg$init <- xreg$init[-exc]

## -----------------------------------------------------------------------------
spec_constant <- issm_modelspec(y["2014/"], slope = FALSE, seasonal = FALSE, ar = 2, ma = 0, xreg = xreg$xreg,
                       lambda = 0, variance = "constant", distribution = "jsu")
spec_constant$parmatrix[grepl("^kappa", parameters), initial := xreg$init]
mod_constant <- estimate(spec_constant, scores = FALSE)

spec_dynamic <- issm_modelspec(y["2014/"], slope = TRUE, seasonal = FALSE, ar = 1, ma = 0, xreg = xreg$xreg,
                       lambda = 0, variance = "dynamic", distribution = "jsu")
spec_dynamic$parmatrix[grepl("^kappa",parameters), initial := xreg$init]
mod_dynamic <- estimate(spec_dynamic, scores = FALSE)
print(paste0("AIC(Dynamic): ", round(AIC(mod_dynamic),1)," | AIC(Constant): ", round(AIC(mod_constant),1)))

## -----------------------------------------------------------------------------
mod_dynamic |> summary() |> as_flextable()

## ----fig.width=6,fig.height=6-------------------------------------------------
plot(mod_dynamic)