dregar6 {DREGAR}

Estimating adaptive/non-adaptive DREGAR coefficients using an iterative 6-step procedure

Description

Estimating coefficients for penalized dynamic regression in the presence of autocorrelated residuals using an iterative 6-step procedure.

Usage

dregar6(data , da, ar, mselection = 4, type = "alasso", 
      normalize = FALSE, iteration = 15,  intercept=FALSE)

Arguments

data

Data matrix of order (time, response, covariates)

da

A vector of lags. Autoregressive orders for the 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)

type

Type of penalty. Choosing between 'enet' and 'alasso' for DREGAR and adaptive-DREGAR penalties.

normalize

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

iteration

The number of iterations

intercept

Logical flag. Setting to TRUE to have intercept in the model.

Author(s)

Hamed Haselimashhadi <[e-mail]>

See Also

dregar2, generateAR , sim.dregar

Examples

 par(mfrow=c(2,2))
  m=sim.dregar(n=500 ,  beta=1:4, phi=generateAR(2), theta=.1, 
               n.z.coeffs=3 , plot=TRUE) # generating data
  r=dregar6(data=m$rawdata, da = 1:3,
      ar = 1:2,mselection = 4,
      type='alasso')# estimating parameters using (non-apdative) DREGAR 
  round(cbind(
    true      = c(phi=c(m$phi,0),theta=c(m$theta,0),beta=m$beta),
    estimates = c(phi=r$phi,theta=r$theta,beta=r$beta)
  )
  ,3
  )
  plot(r$mod.phi,main='phi')
  plot(r$mod.theta,main='theta')
  plot(r$mod.beta,main='beta')