Regression Models for Nonstationary Categorical Time Series: Asymptotic Estimation Theory
非平稳分类时间序列回归模型：渐近估计理论
For the analysis of nonstationary categorical time series, a parsimonious and flexible class of models is proposed. These models are generalizations of regression models for stochastically independent categorical observations. Consistency, asymptotic normality and efficiency of the maximum likelihood estimator are shown under weak and easily verifiable requirements. Some models for binary time series are discussed in detail. To demonstrate asymptotic properties, a theorem is…