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Strong Consistency for the Conditional Self-weighted M Estimator of GRCA(p) Models

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  • Chi Yao

    (Anhui University)

  • Wei Yu

    (Anhui University)

  • Xuejun Wang

    (Anhui University)

Abstract

In this paper, we investigate the strong consistency for the conditional self-weighted M (SM, for short) estimator of generalized random coefficient autoregressive (GRCA(p), for short) model under some general assumptions. We obtain different estimators by taking different loss functions for the conditional SM estimation. What’s more, the simulation studies are provided by conducting three types of GRCA models. We allow that the variance of errors is finite or infinite, the numerical simulations are still valid. In addition, a real data example is given.

Suggested Citation

  • Chi Yao & Wei Yu & Xuejun Wang, 2023. "Strong Consistency for the Conditional Self-weighted M Estimator of GRCA(p) Models," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-21, March.
    • Handle: RePEc:spr:metcap:v:25:y:2023:i:1:d:10.1007_s11009-022-09975-w
    DOI: 10.1007/s11009-022-09975-w
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    References listed on IDEAS

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