DOI QR코드

DOI QR Code

An Analysis of Factors Affecting the Variation of GDP Gap by a Decomposition Method

GDP갭 분해기법을 이용한 변동요인 분석

  • Chang, Youngjae (Department of Information Statistics, Korea National Open University)
  • 장영재 (한국방송통신대학교 정보통계학과)
  • Received : 2014.01.17
  • Accepted : 2014.03.17
  • Published : 2014.06.30

Abstract

The GDP gap (also called the output gap) is the difference between potential GDP and actual GDP. Potential GDP is the maximum sustainable output that is achieved when the resources (labor and capital) are used to capacity. Central banks pursuing price and employment stability consider the output gap as an informative variable for monetary policy since the output gap could be regarded as a proxy of demand-supply imbalances. In this paper, the GDP gap of Korea is decomposed following the filtering method in the previous research, and major factors that affect the variation of GDP gap are investigated based on the decomposed series. The analysis results by the Super Smoother algorithm used in Fox et al. (2003)and Fox and Zurlinden (2006) are found consistent with theory. Much of the variation of nominal GDP gap is explained by Total Factor Productivity(TFP) gap, which is the change of productivity due to recent technological innovation and environmental change. It is also found that variation of terms of trade significantly affects the GDP gap of Korea due to its high dependency on international trade; however, the effect of the domestic price is not negligible like other countries.

References

  1. Sherbaz, S., Amjad, F. and Khan, N. Z. (2009). Output gap and its determinants: Evidence from Pakistan (1964-05), Journal of Economic Cooperation and Development, 30, 75-98.
  2. Park, Y., Chang, Y., Koo, J. and Kim, H. (2013). Uncertainty of GDP gap estimation and Monetary Policy, Monthly Bulletin, The Bank of Korea, April 2013, 14-33.
  3. Schmidt, F. L. (1971). The relative efficiency of regression and simple unit predictor weights in applied differential psychology, Educational and Psychological Measurement, 31, 699-714. https://doi.org/10.1177/001316447103100310
  4. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions, Journal of the Royal Statistical Society: Series B, 36, 111-147.
  5. Tornqvist, L. (1936). The Bank of Finland's Consumption Price Index, Bank of Finland Monthly Bulletin, 10, 1-8.
  6. Allen, R. C. and Diewert, W. E. (1981). Direct versus Implicit Superlative Index Number Formulae, The Review of Economics and Statistics, 63, 430-435. https://doi.org/10.2307/1924361
  7. and overseas investment using a regression tree, The Korean Journal of Applied Statistics, 24, 455-464. https://doi.org/10.5351/KJAS.2011.24.3.455
  8. Fox, K. J., Kohli, U. and Warren, R. S. (2003). Sources of growth and output gaps in New Zealand: New methods and evidence, New Zealand Economic Papers, 37, 67-92. https://doi.org/10.1080/00779950309544379
  9. Fox, K. J. and Zurlinden, M. (2006). On understanding sources of growth and output. Gaps for Switzerland, Swiss National Bank Working Papers, 2006-10.
  10. Friedman, J. H. (1984). A variable span scatterplot smoother, Laboratory for Computational Statistics, Stanford University Technical Report, No. 5.
  11. Lee, S. and Han, M. (2007). The Influence of GDP Gap on Inflation, Monthly Bulletin, The Bank of Korea, November 2007, 23-52.