Browse > Article
http://dx.doi.org/10.5351/KJAS.2015.28.1.103

A Study on Small Business Forecasting Models and Indexes  

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)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.1, 2015 , pp. 103-114 More about this Journal
Abstract
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.
Keywords
Economic Index; Business Survey Index; Stock and Watson Single Index; Chicago Fed National Activity Index(CFNAI); Principle Component Analysis(PCA);
Citations & Related Records
연도 인용수 순위
  • Reference
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.   DOI   ScienceOn
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.