Minimum distance estimation in linear models with long-range dependent errors
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.
Year of publication: |
1994
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Authors: | Mukherjee, Kanchan |
Published in: |
Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 21.1994, 5, p. 347-355
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Publisher: |
Elsevier |
Keywords: | Asymptotic uniform quadraticity Long-range dependence Hermite ranks and polynomials |
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