Construction business cycle analysis using the regime switching model

Jaehyun Park, Seungchul Ham, Taehoon Hong

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The construction industry is a key industry in many countries, usually making up to 5-10% of the overall gross domestic product (GDP). It is closely related to the financial and labor markets, depending on the characteristics of businesses in a given country. For example, the moratorium in Russia in 1998 and the subprime mortgage crisis in the U.S. in 2007 greatly influenced the financial markets of many countries, which consequently affected the construction market. The effect of such crises on the construction industry differs, however, depending on the size of the business cycle and the foundation of the financial market. Thus, this study analyzed the construction business cycle of three countries: the United States, the United Kingdom, and South Korea. The economies of these three countries have different characteristics. This study, which used the three-state Markov switching model, also used construction industry data for categorizing GDP by economic activity. Although the validation results of the U.S. construction industry were unsatisfactory because of the unprecedented long-term recession, results of the analysis showed that the proposed model could be used to determine the construction business cycle. The forecasting performance test also showed that the proposed model could be used to predict more than one quarter in advance, which was the interval in identifying the business cycle. Accordingly, it is believed that the proposed methodology can be used to determine and cope with each country's business cycle.

Original languageEnglish
Pages (from-to)362-371
Number of pages10
JournalJournal of Management in Engineering
Issue number4
Publication statusPublished - 2012 Oct 1

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Industrial relations
  • Engineering(all)
  • Strategy and Management
  • Management Science and Operations Research


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