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

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>7/12/1994
<mark>Journal</mark>Statistics and Probability Letters
Issue number5
Number of pages9
Pages (from-to)347-355
Publication StatusPublished
<mark>Original language</mark>English


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.