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Ridge Autoregression Estimation: LS Method

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  • A. K. MD. Ehsanes Saleh
  • Amal F. Ghania

Abstract

In an AR (p)-model, least-squares estimation of the parameters is considered when it is suspected that the parameters may belong to a linear subspace and the estimated covariance matrix is ill-conditioned. Accordingly, we define five estimators and study their properties in an asymptotic setup to discover dominance properties based on asymptotic distributional bias (ADB), MSE (ADMSE) matrices, and under quadratic risks (ADQR).

Suggested Citation

  • A. K. MD. Ehsanes Saleh & Amal F. Ghania, 2015. "Ridge Autoregression Estimation: LS Method," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(15), pages 3303-3320, August.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:15:p:3303-3320
    DOI: 10.1080/03610926.2013.857866
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