ON THE ROBUSTNESS OF MIXTURE MODELS IN THE PRESENCE OF HIDDEN MARKOV REGIMES WITH COVARIATE-DEPENDENT TRANSITION PROBABILITIES
研究了当真实数据生成过程包含隐马尔可夫机制且转移概率依赖协变量时，使用简化的混合模型进行准极大似然估计的稳健性，发现条件分布参数仍可一致估计。
This article studies the robustness of quasi-maximum-likelihood estimation in hidden Markov models when the regime-switching structure is misspecified. Specifically, we examine the case where the data-generating process features a hidden Markov regime sequence with covariate-dependent transition probabilities, but estimation proceeds under a simplified mixture model that assumes regimes are independent and identically distributed. We show that the parameters governing the conditional distribution of the observables can still be consistently estimated under this misspecification, provided certain regularity conditions hold. Our results highlight a practical benefit of using computationally simpler mixture models in settings where regime dependence is complex or difficult to model directly.