Minimum distance estimation in linear models with long-range dependent errors

Kanchan Mukherjee

Statistics & Probability Letters, 1994, vol. 21, issue 5, 347-355

Abstract: This paper discusses the asymptotic representations of a class of L2-distance estimators based on weighted empirical processes in a multiple linear regression model when the errors are a function of stationary Gaussian random variables that are long-range dependent. Unlike the independent errors case, the limiting distributions of the suitably normalized estimators are not always normal. The limiting distributions depend heavily on the Hermite rank of a certain class of random variables. Some 'goodness of fit' tests for specified error distribution are also considered.

Keywords: Asymptotic; uniform; quadraticity; Long-range; dependence; Hermite; ranks; and; polynomials (search for similar items in EconPapers)
Date: 1994
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