Weighted least squares approximate restricted likelihood estimation for vector autoregressive processes Academic Article

abstract

• We derive a weighted least squares approximate restricted likelihood estimator for a k-dimensional pth-order autoregressive model with intercept. Exact likelihood optimization of this model is generally infeasible due to the parameter space, which is complicated and high-dimensional, involving pk 2 parameters. The weighted least squares estimator has significantly reduced bias and mean squared error than the ordinary least squares estimator for both stationary and nonstationary processes. Furthermore, at the unit root, the limiting distribution of the weighted least squares approximate restricted likelihood estimator is shown to be the zero-intercept Dickey-Fuller distribution, unlike the ordinary least squares with intercept estimator that has a different distribution with significantly higher bias. 2010 Biometrika Trust.

authors

• Chen, Willa

published proceedings

• Biometrika

author list (cited authors)

• Chen, W. W., & Deo, R. S.

citation count

• 9

complete list of authors

• Chen, WW||Deo, RS

publication date

• March 2010

publisher

• Oxford University Press (OUP)  Publisher

published in

• Biometrika  Journal

keywords

• AUTOREGRESSIVE PROCESS
• Bias
• Restricted Maximum Likelihood
• Unbiased Estimating Equation

Digital Object Identifier (DOI)

• 10.1093/biomet/asp071

start page

• 231

end page

• 237

volume

• 97

issue

• 1