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Composite indirect inference with application to corporate risks

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  • Gourieroux, C.
  • Monfort, A.

Abstract

It is frequent to deal with parametric models that are difficult 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 simplified 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

  • Gourieroux, C. & Monfort, A., 2018. "Composite indirect inference with application to corporate risks," Econometrics and Statistics, Elsevier, vol. 7(C), pages 30-45.
    • Handle: RePEc:eee:ecosta:v:7:y:2018:i:c:p:30-45
    DOI: 10.1016/j.ecosta.2017.09.003
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    Cited by:

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

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