Scientific article

Robust Indirect Inference

Published inJournal of the American Statistical Association, vol. 98, no. 461, p. 67-76
Publication date2003

In this article we develop robust indirect inference for a variety of models in a unified framework. We investigate the local robustness properties of indirect inference and derive the influence function of the indirect estimator, as well as the level and power influence functions of indirect tests. These tools are then used to design indirect inference procedures that are stable in the presence of small deviations from the assumed model. Although indirect inference was originally proposed for statistical models whose likelihood is difficult or even impossible to compute and/or to maximize, we use it here as a device to robustify the estimators and tests for models where this is not possible or is difficult with classical techniques such as M estimators. Examples from financial applications, time series, and spatial statistics are used for illustration.

  • Correlated observations
  • Influence function
  • Robustness of validity
  • Robustness of efficiency
  • Space-time autoregression
  • Stochastic differential equations
Citation (ISO format)
GENTON, Marc, RONCHETTI, Elvezio. Robust Indirect Inference. In: Journal of the American Statistical Association, 2003, vol. 98, n° 461, p. 67–76. doi: 10.1198/016214503388619102

Technical informations

Creation09/21/2012 12:06:00 PM
First validation09/21/2012 12:06:00 PM
Update time03/14/2023 5:41:23 PM
Status update03/14/2023 5:41:23 PM
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