The Analysis of Factors which Affect Business Survey Index Using Regression Trees

회귀나무를 이용한 기업경기실사지수의 영향요인 분석

Chang, Young-Jae

  • Received : 20091200
  • Accepted : 20091200
  • Published : 2010.02.28


Business entrepreneurs reflect their views of domestic and foreign economic activities on their operation for the growth of their business. The decision, forecasting, and planning based on their economic sentiment affect business operation such as production, investment, and hiring and consequently affect condition of national economy. Business survey index(BSI) is compiled to get the information of business entrepreneurs' economic sentiment for the analysis of business condition. BSI has been used as an important variable in the short-term forecasting models for business cycle analysis, especially during the the period of extreme business fluctuations. Recent financial crisis has arised extreme business fluctuations similar to those caused by currency crisis at the end of 1997, and brought back the importance of BSI as a variable for the economic forecasting. In this paper, the meaning of BSI as an economic sentiment index is reviewed and a GUIDE regression tree is constructed to find out the factors which affect on BSI. The result shows that the variables related to the stability of financial market such as kospi index(Korea composite stock price index) and exchange rate as well as manufacturing operation ratio and consumer goods sales are main factors which affect business entrepreneurs' economic sentiment.


Business survey index;economic sentiment index;economic forecasting;regression tree


  1. 김종욱 (2000). 상태공간구조에서 기업경기조사 결과의 예측력 평가, <경제분석>, 6, 133-161.
  2. 이긍희 (1999). 외환위기하의 경제예측-기업경기실사지수를 이용한 GDP 단기예측-, <응용통계연구>, 12, 397-404.
  3. 한국은행 (2003). <기업경기조사의 이해>, 한국은행.
  4. Bergstrom, R. (1995). The relationship between manufacturing production and different business survey series in Sweden, International Journal of Forecasting, 11, 379-393.
  5. Breiman, L., Friedman, J., Stone, C. and Olshen, R. A. (1984). Classification and Regression Trees, Chapman & Hall/CRC.
  6. Kauppi, E., Lassila, J. and Terasvirta, T. (1996). Short-term forecasting of industrial production with business survey data: Experience from Finland's great depression 1990-1993 International Journal of Forecasting, 12, 373-381.
  7. Loh, W. -Y. (2002). Regression trees with unbiased variable selection and interaction detection, Statistica Sinica, 12, 361-386.

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