Inference in Coarsened Time Series via Generalized Method of Moments
研究了通过广义矩方法对粗化时间序列进行统计推断，提出了基于多个潜在结果的新模型，并推导了估计量的渐近性质，应用于美国空气质量数据。
We study statistical inference procedures in coarsened time series through the generalized method of moments. A new model for the coarsened time series via multiple potential outcomes is proposed. It can be naturally extended for inferring multi‐variate coarsened time series. We show that this framework generates a general class of estimators. It neatly generalizes the classical Horvitz–Thompson estimator for handling coarsened time series data. Asymptotic properties, including consistency and limiting distribution, of the proposed estimators are investigated. Estimators of the optimal weight matrix and the long‐run covariance matrix are also derived. In particular, confidence intervals of the mean function of the potential outcome as a function of coarsening index can be constructed. A real‐data application on air quality in the USA is investigated.