Inference for jump models and nonlinear inverse problems (C12)

我们重点讨论了跳跃和非线性对经济模型的推断和校准的影响。目前，标准的连续时间动力学模型被扩展到包括随机跳跃，这些随机跳跃代表了对整个经济或金融市场中某些资产的冲击。将这些跳跃模型调整为经验数据的统计工具目前正在开发中。通常，这些方法会导致统计逆问题，这些问题本质上是非线性和不适定的，并且会带来困难的数学和实际问题。

We focus on the effects that jumps and nonlinearities have on the inference in and calibration of economic models. Currently, standard continuous-time dynamical models are extended to include random jumps which represent shocks to the economy as a whole or to some assets in financial markets. Statistical tools to adjust these jump models to empirical data are currently under development. Typically, these approaches lead to statistical inverse problems, which are inherently nonlinear and ill-posed, and will raise difficult mathematical and practical problems.