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Order of convergence of regression parameter estimates in models with infinite variance

Author

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  • Le Breton, A.
  • Musiela, M.

Abstract

A semimartingale driven continuous time linear regression model is studied. Assumptions concerning errors allow us to consider also models with infinite variance. The order of the almost sure convergence of a class of estimates which includes least squares estimates is given. In the presence of errors with heavy tails a modification of least squares estimates is suggested and shown to be better than the latter.

Suggested Citation

  • Le Breton, A. & Musiela, M., 1989. "Order of convergence of regression parameter estimates in models with infinite variance," Journal of Multivariate Analysis, Elsevier, vol. 31(1), pages 59-68, October.
    • Handle: RePEc:eee:jmvana:v:31:y:1989:i:1:p:59-68
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    Cited by:

    1. Wu, Tiee-Jian & Wasan, M. T., 1996. "Weighted least squares estimates in linear regression models for processes with uncorrelated increments," Stochastic Processes and their Applications, Elsevier, vol. 64(2), pages 273-286, November.
    2. Dzhaparidze, K. & Spreij, P., 1989. "On SLLN for martingales with deterministic variation," Serie Research Memoranda 0079, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.

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