Weak consistency of least-squares estimators in linear models

D. Kaffes and M. Bhaskara Rao

Journal of Multivariate Analysis, 1982, vol. 12, issue 2, 186-198

Abstract: Let Yn, n>=1, be a sequence of integrable random variables with EYn = xn1[beta]1 + xn2[beta]2 + ... + xnp[beta]p, where the xij's are known and [beta]T = ([beta]1, [beta]2,..., [beta]p) unknown. Let bn be the least-squares estimator of [beta] based on Y1, Y2,..., Yn. Weak consistency of bn, n>=1, has been considered in the literature under the assumption that each Yn is square integrable. In this paper, we study weak consistency of bn, n>=1, and associated rates of convergence under the minimal assumption that each Yn is integrable.

Keywords: Linear; models; convergence; in; probability; weak; consistency; estimable; linear; functions; rates; of; convergence (search for similar items in EconPapers)
Date: 1982
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Citations: View citations in EconPapers (2)

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