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 ($\varphi_1, \dots, \varphi_p$).
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

Details

The AR process is defined by $$\Phi\left(B\right)y_t=\epsilon_t$$ where $$\Phi\left(B\right)=1+\varphi_1 B + \cdots + \varphi_p B^p$$ is an auto-regressive polynomial.