Empirical processes for infinite variance autoregressive models
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.Download Info
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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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Keywords: Empirical process; Stable distributions; Infinite variance; Autoregressive models; Independence tests; Goodness-of-fit tests; Portmanteau statistics;References
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