dregar2 {DREGAR}

Estimating (just) adaptive-DREGAR coefficients using an iterative 2-step procedure

Description

Estimating coefficients for penalized/non-penalized dynamic regression in the presence of autocorrelated residuals using an iterative 2-step procedure.

Usage

dregar2(data, da = 0, ar = 0, mselection = 4, 
       normalize = FALSE, penalized = TRUE, 
       iteration = 15)

Arguments

data

Data matrix of order (time, response, covariates)

da

A vector of lags. Autoregressive orders for response. For example 1:p for all lags from 1 to p

ar

A vector of lags. Autoregressive orders for residuals. For example 1:q for all lags from 1 to q

mselection

Model selection criteria. Choosing among 1 (CP), 2 (AIC), 3 (GCV) and 4 (BIC)

normalize

Logical flag. Setting to TRUE to normalize data prior to analysis

penalized

Logical flag. Setting to TRUE to estimate coefficients through penalized likelihood. Otherwise the algorithm applies iterative OLS.

iteration

The number of iterations

Author(s)

Hamed Haselimashhadi <[e-mail]>

See Also

dregar6 , generateAR , sim.dregar

Examples

par(mfrow=c(2,2))
  m=sim.dregar(n=500 ,  beta=1:4, phi=generateAR(2), theta=.3, 
               n.z.coeffs=3 , plot=TRUE) # generating data
  r=dregar2(data = m$rawdata,da = 1:3,ar = 1:2,mselection = 4,
            penalized = 1 )# estimating parameters using2-step adaptive-DREGAR
  round(cbind(
    true      = c(phi=c(m$phi,0),theta=c(m$theta,0),beta=m$beta),
    estimates = c(phi=r$phi,theta=r$theta,beta=r$beta)
  )
  ,3
  )
  plot(r$obj)
  acf(r$res, main='Residual ACF')
  pacf(r$res,main='Residual PACF')