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A uniform weak law of large numbers under π‐mixing with application to nonlinear least squares estimation

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  • H. J. Bierens

Abstract

In Bierens (1981) we have derived a uniform weak law of large numbers for stochastically stable processes with respect to a finite‐dependent base. In this paper we show that this uniform weak law carries over to stochastically stable processes with respect to a, more general, φ‐mixing base. This generalization will be used for relaxing the conditions for weak consistency and asymptotic normality of nonlinear least squares estimators.

Suggested Citation

  • H. J. Bierens, 1982. "A uniform weak law of large numbers under π‐mixing with application to nonlinear least squares estimation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(2), pages 81-86, June.
    • Handle: RePEc:bla:stanee:v:36:y:1982:i:2:p:81-86
    DOI: 10.1111/j.1467-9574.1982.tb00777.x
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    Cited by:

    1. Calzolari, Giorgio, 1992. "Stima delle equazioni simultanee non-lineari: una rassegna [Estimation of nonlinear simultaneous equations: a survey]," MPRA Paper 24123, University Library of Munich, Germany, revised 1992.
    2. Gopal K. Basak & Arnab Bhattacharjee & Samarjit Das, 2018. "Causal ordering and inference on acyclic networks," Empirical Economics, Springer, vol. 55(1), pages 213-232, August.
    3. Ian Domowitz, 1985. "New Directions in Non-linear Estimation with Dependent Observations," Canadian Journal of Economics, Canadian Economics Association, vol. 18(1), pages 1-27, February.

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