Robust Indirect Inference for Stochastic Differential Equations

• Activity Number: 76
• Type: Contributed
• Date/Time: Monday, August 12, 2002 : 8:30 AM to 10:20 AM
• Sponsor: General Methodology
• Author(s): Marc Genton*+ and Elvezio Ronchetti
• Affiliation(s): North Carolina State University and University of Geneva
• Keywords: Correlated observations ; Influence function ; Auxiliary model ; Robustness ; Stochastic differ ential equations ; Finance

Abstract

We develop robust indirect inference for a variety of models in a unified framework. We investigate the local robustness properties of indirect inference, and we 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 which 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 difficult with classical techniques such as M-estimators. An application to stochastic differential equations is used for illustration.