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Composite Indirect Inference with Application to Corporate Risks

Author

Listed:
  • Christian Gouriéroux

    (CREST, University of Toronto)

  • Alain Monfort

    (CREST,Banque de France)

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 of this paper 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 our paper provides a bridge between the econometric literature on indirect inference and the statistical literature on composite likelihood. The composite indirect inference principle is illustrated by an application to the analysis of corporate risks.

Suggested Citation

  • Christian Gouriéroux & Alain Monfort, 2016. "Composite Indirect Inference with Application to Corporate Risks," Working Papers 2016-32, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2016-32
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    Cited by:

    1. Antoine Djogbenou & Christian Gouriéroux & Joann Jasiak & Maygol Bandehali, 2024. "Composite Likelihood for Stochastic Migration Model with Unobserved Factor," Journal of Financial Econometrics, Oxford University Press, vol. 22(5), pages 1421-1455.
    2. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.
    3. Gourieroux, Christian & Jasiak, Joann, 2018. "Misspecification of noncausal order in autoregressive processes," Journal of Econometrics, Elsevier, vol. 205(1), pages 226-248.
    4. Christian Gouriéroux & Alain Monfort & Eric Renault, 2017. "Consistent Pseudo-Maximum Likelihood Estimators," Annals of Economics and Statistics, GENES, issue 125-126, pages 187-218.

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