Multivariate Trend‐Cycle‐Seasonal Decompositions with Correlated Innovations*
提出一种多变量不可观测成分模型，用于季度数据，允许趋势、周期和季节成分在不同变量间相关，并利用经济约束（如共同趋势、共同周期、共同季节）辅助识别，以意大利GDP和消费数据为例说明。
Abstract Multivariate analysis can help to focus on important phenomena, including trend and cyclical movements, but any economic information in seasonality is typically ignored. The present paper aims to more fully exploit time series information through a multivariate unobserved component model for quarterly data that exhibits seasonality together with cross‐variable component correlations. We show that economic restrictions, including common trends, common cycles and common seasonals can aid identification. The approach is illustrated using Italian GDP and consumption data.