dregar: Estimating (just) adaptive-DREGAR coefficients using an...

In DREGAR: Regularized Estimation of Dynamic Linear Regression in the Presence of Autocorrelated Residuals (DREGAR)

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')

DREGAR documentation built on May 2, 2019, 7:59 a.m.