Multivariate Trend-Cycle-Seasonal Decompositions with Correlated Innovations

被引：0
|
作者：
Tian, Jing [1 ,2 ]
Jacobs, Jan P. A. M. [2 ,3 ,4 ]
Osborn, Denise R. [2 ,5 ]
机构：
[1] Univ Tasmania, Tasmanian Sch Business & Econ, Econ, Sand Bay, Tas, Australia
[2] CAMA, Acton, ACT, Australia
[3] Univ Groningen, Fac Econ & Business, POB 800, NL-9700 AV Groningen, Netherlands
[4] CIRANO, Montreal, PQ, Canada
[5] Univ Manchester, Manchester, England
来源：
OXFORD BULLETIN OF ECONOMICS AND STATISTICS | 2024年 / 86卷 / 05期
关键词：
BUSINESS; PERMANENT; OUTPUT; MODEL;
D O I：
10.1111/obes.12602
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
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.
引用
收藏
页码：1260 / 1289
页数：30
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