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Composite Indirect Inference with Application

Author

Listed:
  • Christian Gouriéroux

    (CREST; University of Toronto)

  • Alain Monfort

    (CREST)

Abstract

It is frequent to deal with parametric models which are di cult to analyze, due to the large number of data and/or parameters, complicated nonlinearities, or unobservable variables. The aim is to explain how to analyze such models by means of a set of simpli ed models, called instrumental models, and how to combine these instrumental models in an optimal way. In this respect a bridge between the econometric literature on indirect inference and the statistical literature on composite likelihood is provided. The composite indirect inference principle is illustrated by an application to the analysis of corporate risks.

Suggested Citation

  • Christian Gouriéroux & Alain Monfort, 2017. "Composite Indirect Inference with Application," Working Papers 2017-07, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2017-07
    as

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    References listed on IDEAS

    as
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    7. D. R. Cox, 2004. "A note on pseudolikelihood constructed from marginal densities," Biometrika, Biometrika Trust, vol. 91(3), pages 729-737, September.
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    Cited by:

    1. Blasques, F. & Gorgi, P. & Koopman, S.J., 2021. "Missing observations in observation-driven time series models," Journal of Econometrics, Elsevier, vol. 221(2), pages 542-568.

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