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论文类型：期刊论文

发表时间：2016-06-01

发表刊物：KNOWLEDGE-BASED SYSTEMS

收录刊物：SCIE、EI、Scopus

卷号：101

页面范围：135-141

ISSN号：0950-7051

关键字：Business cycles; Composite indicator; Information granulation; Dynamic Time Warping (DTW); Particle Swarm Optimization (PSO)

摘要：Composite indicators of business cycles play a paramount role in the analysis of macroeconomy, which provide decision makers with much meaningful information. This paper develops a novel constructing method of the business cycle composite indicator based on information granulation and Dynamic Time Warping (DTW), which not only takes the most important indicator real Gross Domestic Product (GDP) into account but avoids some impractical assumptions in dynamic factor models. First, the quarterly real GDP sequence is divided into information granules by the principle of justifiable granularity. Next, monthly coincident indicators are split into corresponding segments relying on the information granules of real GDP, and DTW is applied to measure the similarity between monthly and quarterly segments. The weights are derived by normalizing reciprocals of the above distance values. Finally, the monthly composite indicator of business cycles is obtained by taking a weighted cross-section average of those monthly coincident indicators. The numerical experiment reveals that the composite indicator established by the proposed method can reflect the dynamics of business cycles and accurately catch the turning points in business cycles. (C) 2016 Published by Elsevier B.V.