Autoregressive model — ar • rjd3sts

Functions to create an autoregressive model (ar) or a modified autoregressive model (ar2)

Usage

ar(
  name,
  ar,
  fixedar = FALSE,
  variance = 0.01,
  fixedvariance = FALSE,
  nlags = 0,
  zeroinit = FALSE
)

ar2(
  name,
  ar,
  fixedar = FALSE,
  variance = 0.01,
  fixedvariance = FALSE,
  nlags = 0,
  nfcasts = 0
)

Arguments

ar: vector of the AR coefficients (See @details Additional details...).
fixedar: boolean that triggers the estimation of the AR coefficients (FALSE) or fixed it (TRUE) to a pre-specified value set by the parameter ar.
variance: the variance ($\sigma^2_{ar}$).
fixedvariance: boolean that triggers the estimation of the variance (FALSE) or fixed it (TRUE) to a pre-specified value set by the parameter variance.
nlags: integer specifying how many lags of the state variable are needed
zeroinit: boolean determining the initial condition for the state variable, which is equal to zero if zeroinit = TRUE. The default (zeroinit = FAKSE) triggers the an initialization based on the unconditional mean and variance of the AR(p) process.
nfcasts: integer specifying how many forecasts of the state variable are needed

Value

An element of type "JD3_SsfStateBlock" (wrapper around the corresponding Java object))

Details

The AR process is defined by $$y_t = \phi_1 y_{t-1} + \phi_2 y_{t-2} + \dots + \phi_p y_{t-p} + \epsilon_t$$ The stability of the auto-regressive polynomial is not checked.

Examples

b_ar<-ar("my_ar", c(.8,-.3,.2), variance=1)
block_p0(b_ar)
#>           [,1]     [,2]      [,3]
#> [1,] 1.9441499 1.307883 0.7246377
#> [2,] 1.3078826 1.944150 1.3078826
#> [3,] 0.7246377 1.307883 1.9441499