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Title: Local asymptotic normality for ergodic jump diffusion processes Authors:  Yuma Uehara - Kansai University (Japan) [presenting]
Teppei Ogihara - University of Tokyo (Japan)
Abstract: Sufficient conditions for local asymptotic mixed normality are studied in order to deal with a wider class of statistical models. Moreover, we show that the local asymptotic mixed normality of a statistical model generated by approximated transition density functions is implied for the original model. Together with a density approximation by means of thresholding techniques, we derive the local asymptotic normality for a statistical model of discretely observed jump-diffusion processes where the drift coefficient, diffusion coefficient, and jump structure are parametrized. As a consequence, the quasi maximum likelihood and Bayes type estimators are shown to be asymptotically efficient in this model.