DOI QR코드

DOI QR Code

중소기업 경기예측 모형 및 지수에 관한 연구

A Study on Small Business Forecasting Models and Indexes

  • 윤여창 (우석대학교 정보보안학과) ;
  • 이성덕 (충북대학교 정보통계학과) ;
  • 성재현 (충북대학교 정보통계학과)
  • Yoon, YeoChang (Department of Information security, Woosuk University) ;
  • Lee, Sung Duck (Department of Information and Statistics, Chungbuk National University) ;
  • Sung, JaeHyun (Department of Information and Statistics, Chungbuk National University)
  • 투고 : 2015.01.13
  • 심사 : 2015.01.29
  • 발행 : 2015.02.28

초록

경제의 새로운 성장요인으로 중소기업의 역할이 부각됨에 따라 중소기업의 경기를 적절히 파악할 수 있는 지표 개발의 필요성이 증대되고 있다. 현재 여러 기관에서 발표하는 중소기업 경기와 관련된 지표들은 대부분 BSI(Business survey index)에 기초하고 있고 주관적 지표에 의존하고 있어 정확한 경기 상황을 충분히 반영한다고 볼 수 없다. 본 연구에서 제시한 새로운 경기지표는 주성분 분석과 가중치 방법으로 통계청의 기준순환 일에 의한 경기 국면을 적절히 반영하고 있다. 제안된 새로운 경기지수는 경기종합지수와 유사한 추세를 보이면서 통계학적 이론에 충실한 지표임을 실증사례 연구로부터 입증한다.

The role of small and medium enterprises as an economic growth factor has been accentuated; consequently, the need to develop a business forecast model and indexes that accurately examine business situation of small and medium enterprises has increased. Most current business model and indexes concerning small and medium enterprises, released by public and private institutions, are based on Business Survey Index (BSI) and depend on subjective (business model and) indexes; therefore, the business model and indexes lack a capacity to grasp an accurate business situation of these enterprises. The business forecast model and indexes suggested in the study have been newly developed with Principal Component Analysis(PCA) and weight method to accurately measure a business situation based on reference dates addressed by the National Statistical Office(NSO). Empirical studies will be presented to prove that the newly proposed business model and indexes have their basis in statistical theory and their trend that resembles the existing Composite Index.

키워드

참고문헌

  1. Boschan, C. and Banerji, A. (1990). A Reassessment of Composite Indexes., in P.A. Klein, ed., Analyzing Modern Business Cycles : Essays Honoring G.H. Moore, Armonk, New York; M.E. Sharpe. Inc., 207-225.
  2. Cullity, J. and Banerji, A. (1996). Procedures for constructing composite index: A re-assessment, Meeting on OECD Leading Indicators, 17-28.
  3. Green, G. R. and Beckman, B. A. (1992). The composite indexes of coincident indicators and alternative coincident indexes, Survey of Current Business, 72, 42-45.
  4. Green, G. R. and Beckman, B. A. (1993). Business cycle indicators: Upcoming revision of composite indexes, Survey of Current Business, 73, 44-51.
  5. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and business cycle, Econometrica, 57, 357-384. https://doi.org/10.2307/1912559
  6. Seo, J. D., Lee, S. D., Kim, S. Y. and Kang, I. S. (2004). A study of development on small business forecasting models, KOSBI Research Reports, KOSBI.
  7. Stock, J. H. and Watson, M. W. (1991). A Probability Model of the Coincident Economic indicators, in K. Lahiri & G. H. Moore, eds., Leading Economic indicators, New Approaches and Forecasting Records, Cambridge University Press, 63-85.
  8. Stock, J. H. and Watson, M. W. (1999). Forecasting inflation, Journal of Monetary Economics, 72, 42-45.