The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions

被引:2
|
作者
McElroy, Tucker S. [1 ]
Wildi, Marc [2 ]
机构
[1] US Census Bur, Res & Methodol Directorate, 4600 Silver Hill Rd, Washington, DC 20233 USA
[2] Zurich Univ Appl Sci, IDP, Rosenstr, CH-8401 Winterthur, Switzerland
关键词
Frequency Domain; Seasonality; Time Series; Trends; SIGNAL EXTRACTION; TIME-SERIES; REGRESSION; FORMULAS; ERROR;
D O I
10.1016/j.ecosta.2019.12.004
中图分类号
F [经济];
学科分类号
02 ;
摘要
Numerous contexts in macroeconomics, finance, and quality control require real-time estimation of trends, turning points, and anomalies. The real-time signal extraction problem is formulated as a multivariate linear prediction problem, the optimal solution is presented in terms of a known model, and multivariate direct filter analysis is proposed to address the more typical situation where the process' model is unknown. It is shown how general constraints - such as level and time shift constraints - can be imposed on a concurrent filter in order to guarantee that real-time estimates have requisite properties. The methodology is applied to petroleum and construction data. Published by Elsevier B.V. on behalf of EcoSta Econometrics and Statistics.
引用
收藏
页码:112 / 130
页数:19
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