{"title":"Multivariate Trend-Cycle-Seasonal Decompositions with Correlated Innovations*","authors":"Jing Tian, Jan P.A.M. Jacobs, Denise R. Osborn","doi":"10.1111/obes.12602","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":54654,"journal":{"name":"Oxford Bulletin of Economics and Statistics","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/obes.12602","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oxford Bulletin of Economics and Statistics","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/obes.12602","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
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.
多变量分析有助于关注重要现象，包括趋势和周期性变动，但季节性中的任何经济信息通常都会被忽视。本文旨在通过针对季度数据的多变量无观测成分模型，更充分地利用时间序列信息，该模型显示了季节性和跨变量成分相关性。我们表明，经济限制（包括共同趋势、共同周期和共同季节性）有助于识别。我们使用意大利的 GDP 和消费数据对这一方法进行了说明。
期刊介绍：
Whilst the Oxford Bulletin of Economics and Statistics publishes papers in all areas of applied economics, emphasis is placed on the practical importance, theoretical interest and policy-relevance of their substantive results, as well as on the methodology and technical competence of the research.
Contributions on the topical issues of economic policy and the testing of currently controversial economic theories are encouraged, as well as more empirical research on both developed and developing countries.
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