DOI QR코드

DOI QR Code

Predictability of Consumer Expectations for Future Changes in Real Growth

소비자 기대심리의 미래 성장 예측력

  • Kim, Tae-Ho (Department of Information Statistics, Chungbuk National University) ;
  • Lim, La-Hee (Statistics Korea) ;
  • Lee, Seung-Eun (Department of Information Statistics, Chungbuk National University)
  • Received : 2015.02.03
  • Accepted : 2015.04.09
  • Published : 2015.06.30

Abstract

The long lasting world-wide recession and low economic progress have made it more important to predict future economic behavior. Accordingly, it is of interest to explore useful leading indicators, correlated with policy targets, to predict future economic growth. This study attempts to develop a model to evaluate the performance of consumer survey results from Statistics Korea to predict future economic activities. A statistical model is formulated and estimated to generate predictions by utilizing consumer expectations. The prediction is found improved in the distant future and consumer expectations appear to be a useful leading indicator to provide information of future real growth.

경기침체가 장기화되고 세계적으로 저성장이 지속되면서 미래의 경기동향에 대한 예측의 중요성이 증폭되었다. 정부의 정책이 계획되면서부터 효과가 나타나기까지에는 시차가 존재하므로, 정책목표와 선행적 상관관계를 가지면서 목표의 미래 상황을 예측할 수 있는 유용한 지표의 개발에 관심이 모아진다. 본 연구에서는 통계청이 실시한 소비자 전망조사 결과가 미래의 실질성장에 유용한 선행적 정보를 제공했는지 평가해 보았다. 소비자들의 기대심리를 나타내는 체감지표를 사용하여 예측을 유발하는 통계모형을 설정한 후 미래의 실질성장에 대해 유의한 예측력을 갖는지 추정하였다. 소비자기대심리의 예측력은 먼 미래로 갈수록 정확도가 높아져 미래의 실질성장에 대해 선행적 정보를 주는 변수로 활용할 수 있는 것으로 판별된다.

Keywords

References

  1. Baek, M. and Kim, W. (2012). Investigation on Granger causality between economic growth and demand for electricity in Korea: Using quarterly data, The Korean Journal of Applied Statistics, 25, 89-99. https://doi.org/10.5351/KJAS.2012.25.1.089
  2. Bazen, S. and Marimoutou, V. (2002). Looking for a needle in a haystack? A re-examination of the time series relationship between teenage employment and minimum wages in the United States, Oxford Bulletin of Economics and Statistics, 64, 699-725. https://doi.org/10.1111/1468-0084.64.s.7
  3. Davis, E. P. and Fagan, G. (1997). Are financial spreads useful indicators of future inflation and output growth in EU countries?, Journal of Applied Econometrics, 12, 701-714. https://doi.org/10.1002/(SICI)1099-1255(199711/12)12:6<701::AID-JAE456>3.0.CO;2-9
  4. Estrella, A. and Mishkin, F. S. (1998). Predicting U.S. recessions: Financial variables as leading indicators, Review of Economics and Statistics, 80, 45-61. https://doi.org/10.1162/003465398557320
  5. Granger, C. W. J. (1986). Development in the study of cointegrated economic variable, Oxford Bulletin of Economics and Statistics, 48, 213-228.
  6. Griliches, Z. and Rao, P. (1969). Small sample properties of several two-stage regression methods in the context of autocorrelated errors, Journal of the American Statistical Association, 64, 253-272. https://doi.org/10.1080/01621459.1969.10500968
  7. Harvey, C. R. (1988). The real term structure and consumption growth, Journal of Financial Economics, 22, 305-333. https://doi.org/10.1016/0304-405X(88)90073-6
  8. Jang, I. S. (2007). Long-term forecasting for real GDP of Korea: 2007-2050, Economic Issue Brief, No. 22, National Assembly Budget Office.
  9. Jang, I. S. (2010). The effect of aging on productivity and economic growth, Economic Issue Brief, No. 60, National Assembly Budget Office.
  10. Kim, J., Kim, D. and Jung, I. (2011). Analyzing factors of changes in interest rate term structure, BOK Economy Brief, The Bank of Korea, 2011-2.
  11. Kim, T. H. (2011). Impact of the change in market conditions on a test for market cointegration, The Korean Journal of Applied Statistics, 24, 103-114. https://doi.org/10.5351/KJAS.2011.24.1.103
  12. Kim, T. H., Hwang, S. H. and Lee, Y. H. (2005). An analysis for the structural variation in the unemployment rate and the test for the turning point, The Korean Journal of Applied Statistics, 18, 253-269. https://doi.org/10.5351/KJAS.2005.18.2.253
  13. Lee, H. S. (2010). A study on the predictability of economy by the long and short term interest spread of KRW interest rate swap, Journal of Industrial Economics and Business, 23, 3247-3269.
  14. Moon, H. (2011). Construction of an economic sentiment indicator for the Korean economy, The Korean Journal of Applied Statistics, 24, 745-758. https://doi.org/10.5351/KJAS.2011.24.5.745
  15. Newey, W. K. and West, K. D. (1987). A Simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix, Econometrica, 55, 703-708. https://doi.org/10.2307/1913610
  16. Rudebusch, G. D., Sack, B. P. and Swanson, E. T. (2007). Macro-economic implications of changes in the term premium, Federal Reserve Bank of St. Louis Review, 89, 241-269.
  17. Smith, J. and Naylor, R. (2001). Determining of degree performance in UK universities: A statistical analysis of the 1993 student cohort, Oxford Bulletin of Economics and Statistics, 63, 29-60. https://doi.org/10.1111/1468-0084.00208
  18. Stock, J. H. and Watson, M. W. (1996). Evidence on structural instability in macroeconomic time series relations, Journal of Business and Economic Statistics, 14, 11-30.
  19. Stock, J. H. and Watson, M. W. (2002a). Macroeconomic forecasting using diffusion indexes, Journal of Business and Economic Statistics, 20, 147-162. https://doi.org/10.1198/073500102317351921
  20. Stock, J. H. and Watson, M. W. (2002b). Forecasting using principal components from a large number of predictors, Journal of the American Statistical Association, 97, 1167-1179. https://doi.org/10.1198/016214502388618960
  21. Yoo, S. S. (2006). Experience of economic forecasts 2000-2005 and direction of improvement for short term forecasting methods, Economic Issue Brief No.10, National Assembly Budget Office.
  22. Zeileis, A., Kleiber, C., Kramer, W. and Hornik, K. (2003). Testing and dating of structural changes in practice, Computational Statistics and Data Analysis, 44, 109-123. https://doi.org/10.1016/S0167-9473(03)00030-6