标题： 观测驱动模型的可识别性必要充分条件 显示英文标题

标题： Necessary and sufficient conditions for the identifiability of observation-driven models

摘要： 在本次贡献中，我们感兴趣的是证明给定的观测驱动模型是可识别的。 在GARCH(p, q)模型的情况下，[1]中已经建立了一个简单的充分条件，用于证明准最大似然估计量的一致性。 结果表明，这个条件适用于一个更大的观测驱动模型类别，我们称之为线性观测驱动模型类别。 此类别包括标准的整数值观测驱动时间序列，例如对数线性泊松GARCH或NBIN-GARCH模型。 显示英文摘要

摘要： In this contribution we are interested in proving that a given observation-driven model is identifiable. In the case of a GARCH(p, q) model, a simple sufficient condition has been established in [1] for showing the consistency of the quasi-maximum likelihood estimator. It turns out that this condition applies for a much larger class of observation-driven models, that we call the class of linearly observation-driven models. This class includes standard integer valued observation-driven time series, such as the log-linear Poisson GARCH or the NBIN-GARCH models.