Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data
提出多元表示方法，对单变量和双变量周期模型施加共同周期相关特征约束以实现参数简约，并用于分析机场日度到达和离开数据中存量与流量变量的动态交互。
AbstractWe propose the multivariate representation of univariate and bivariate (possibly nonstationary) periodic models as a benchmark for the imposition of common periodic correlation (CPC) feature restrictions to obtain parameter parsimony. CPCs are short-run common dynamic features that co-vary across the different days of the week and possibly also across weeks and that can be common across different time series. We also show how periodic models can be used to describe interesting dynamic links in the interaction between stock and flow variables. We apply the proposed modeling framework to a dataset of daily arrivals and departures in airport transit data.KEY WORDS: CointegrationCommon featuresHigh-frequency dataPeriodic autoregressionSeasonality