Accurate and Robust Indirect Inference for Diffusion Models

Indirect inference (Smith, 1993; Gouriéroux, Monfort and Renault, 1993) is a simulation-based estimation method dealing with econometric models whose likelihood function is intractable. Typical examples are diffusion models described by stochastic differential equations. A potential problem that arises when estimating a diffusion model is the possible model misspecification which can lead to biased estimators and misleading test results. To correct the bias due to model misspecification, Genton and Ronchetti (2003) proposed robust indirect inference. The standard asymptotic approximation to the finite sample distribution of the robust indirect estimators and tests, however, can be very poor and can lead to misleading inference. To improve the finite sample accuracy, we propose in this paper an optimal choice of the auxiliary discretized model and a new test based on asymptotically equivalent M-estimators of the robust indirect estimators. We apply the robust indirect saddlepoint tests using an optimal choice of discretization to various contaminated diffusion models and we illustrate the gain in finite sample accuracy when using the new technique.