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Fixed design regression for time series: Asymptotic normality

Author

Listed:
  • Roussas, George G.
  • Tran, Lanh T.
  • Ioannides, D. A.

Abstract

Consider the fixed regression model with general weights, and suppose that the error random variables are coming from a strictly stationary stochastic process, satisfying the strong mixing condition. The asymptotic normality of the proposed estimate is established under weak conditions. The applicability of the results obtained is demonstrated by way of two existing estimates, the Gasser-Müller estimate and that of Priestley and Chao. The asymptotic normality of these estimates is further illustrated by means of a concrete example from the class of autoregressive processes.

Suggested Citation

  • Roussas, George G. & Tran, Lanh T. & Ioannides, D. A., 1992. "Fixed design regression for time series: Asymptotic normality," Journal of Multivariate Analysis, Elsevier, vol. 40(2), pages 262-291, February.
    • Handle: RePEc:eee:jmvana:v:40:y:1992:i:2:p:262-291
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