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

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  • Mukherjee, Kanchan

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.

Suggested Citation

  • Mukherjee, Kanchan, 1994. "Minimum distance estimation in linear models with long-range dependent errors," Statistics & Probability Letters, Elsevier, vol. 21(5), pages 347-355, December.
    • Handle: RePEc:eee:stapro:v:21:y:1994:i:5:p:347-355
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    Cited by:

    1. Mukherjee, Kanchan, 2000. "Linearization Of Randomly Weighted Empiricals Under Long Range Dependence With Applications To Nonlinear Regression Quantiles," Econometric Theory, Cambridge University Press, vol. 16(3), pages 301-323, June.
    2. Furrer, Reinhard, 2002. "M-Estimation for dependent random variables," Statistics & Probability Letters, Elsevier, vol. 57(4), pages 337-341, May.

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