标题： 观测驱动模型的最大似然估计的可行逆条件 显示英文标题

标题： Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models

摘要： 观测驱动时间序列模型的可逆性条件在实际应用中往往无法得到保证。因此，最大似然估计量和准最大似然估计量的渐近理论可能会受到影响。我们推导出了显著更弱的条件，这些条件可以在实践中用于确保一类广泛的观测驱动时间序列模型的最大似然估计量的一致性。我们的结果对于正确设定和错误设定的模型都成立。理论的实际相关性在一组经验例子中得到了强调。此外，我们还获得了参数空间不可行的“真实”可逆性区域的渐近检验和置信界限。 显示英文摘要

摘要： Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used in practice to ensure the consistency of the maximum likelihood estimator for a wide class of observation-driven time series models. Our consistency results hold for both correctly specified and misspecified models. The practical relevance of the theory is highlighted in a set of empirical examples. We further obtain an asymptotic test and confidence bounds for the unfeasible " true " invertibility region of the parameter space.