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Empirical processes for infinite variance autoregressive models

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  • Bouhaddioui, Chafik
  • Ghoudi, Kilani
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    Abstract

    The paper proposes new procedures for diagnostic checking of fitted models under the assumption of infinite-variance errors which are in the domain of attraction of a stable law. These procedures are functional of residual-based empirical processes. First, the asymptotic distributions of the empirical processes based on residuals are derived. Then two important applications in time series diagnostics are discussed. A goodness-of-fit test is developed using a functional of the empirical process based on residuals. Tests of independence of innovations are also considered. The finite-sample behavior of these tests are studied by simulation and comparison with the classical Portmanteau tests for ARMA models with infinite-variance developed recently by Lin and McLeod (2008) [25] is provided.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 107 (2012)
    Issue (Month): C ()
    Pages: 319-335

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    Handle: RePEc:eee:jmvana:v:107:y:2012:i:c:p:319-335

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    Related research

    Keywords: Empirical process; Stable distributions; Infinite variance; Autoregressive models; Independence tests; Goodness-of-fit tests; Portmanteau statistics;

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