时 间:2019年1月7日 10:00--11:00
地 点:理科楼 LD202
摘 要: Indirect inference requires simulating realisations of endogenous variables from the model under study. When the endogenous variables are discontinuous functions of the model parameters, the resulting indirect inference criterion function is discontinuous and does not permit the use of derivative-based optimisation routines. Using a change of variables technique, we propose a novel simulation algorithm that alleviates the underlying discontinuities inherent in such indirect inference criterion functions, and permits the application of derivative-based optimisation routines to estimate the unknown model parameters. Unlike competing approaches, this approach does not rely on kernel smoothing or bandwidth parameters. Several Monte Carlo examples that have featured in the literature on indirect inference with discontinuous outcomes illustrate the approach. These examples demonstrate that this new method gives superior performance over existing alternatives in terms of bias and variance.
报告人简介:朱丹，莫纳什大学高级讲师，在ASTIN Bulletin、European Journal of Operational Research， Journal of Computational Finance，Applied Mathematical Finance等杂志上发表论文多篇。