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Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models

Listed author(s):
  • F Blasques

    (CREATES)

  • P Gorgi

    (CREATES)

  • S Koopman

    (CREATES)

  • O Wintenberger

    (University of Copenhagen, LSTA)

Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used in practice to ensure the consistency of the maximum likelihood estimator for a wide class of observation-driven time series models. Our consistency results hold for both correctly specified and misspecified models. The practical relevance of the theory is highlighted in a set of empirical examples. We further obtain an asymptotic test and confidence bounds for the unfeasible " true " invertibility region of the parameter space.

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File URL: http://arxiv.org/pdf/1610.02863
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Paper provided by arXiv.org in its series Papers with number 1610.02863.

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Date of creation: Oct 2016
Handle: RePEc:arx:papers:1610.02863
Contact details of provider: Web page: http://arxiv.org/

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  14. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2014. "Maximum Likelihood Estimation for Generalized Autoregressive Score Models," Tinbergen Institute Discussion Papers 14-029/III, Tinbergen Institute, revised 19 Apr 2014.
  15. Andrew Harvey & Alessandra Luati, 2014. "Filtering With Heavy Tails," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1112-1122, September.
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  18. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2012. "Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes," Tinbergen Institute Discussion Papers 12-059/4, Tinbergen Institute.
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