Journal of Energy Engineering (에너지공학)

The Korean Society for Energy (한국에너지학회)
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Estimation of diesel fuel demand function using panel data

시도별 패널데이터를 이용한 경유제품 수요함수 추정

  • Lim, Chansu (Energy Affairs Team, GS Caltex Corp. / Program of Technology Management, Economy and Policy, School of Engineering, Seoul National University)
  • 임찬수 (GS칼텍스 에너지업무팀/서울대학교 공과대학 협동과정 기술경영경제정책)
  • Received : 2017.03.02
  • Accepted : 2017.05.24
  • Published : 2017.06.30
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Abstract

This paper attempts to estimate the diesel fuel demand function in Korea using panel data panel data of 16 major cities or provinces which consist of diesel demands, diesel market prices and gross value added from the year 1998 to 2015. I apply panel GLS(generalized least square) model, fixed effect model, random effect model and dynamic panel model to estimating the parameters of the diesel fuel demand function. The results show that short-run price elasticities of the diesel fuel demand are estimated to be -0.2146(panel GLS), -0.2886(fixed effect), -0.2854(random effect), -0.1905(dynamic panel) respectively. And short-run income elasticities of the diesel fuel demand are estimated to be 0.7379(panel GLS), 0.4119(fixed effect), 0.7260(random effect), 0.4166(dynamic panel) respectively. The short-run price and income elasticities explain that demand for diesel fuel is price- and income-inelastic. The long-run price and income elasticities are estimated to be -0.4784, 1.0461 by dynamic panel model, which means that demand for diesel fuel is price-inelastic but income-elastic in the long run. In addition I apply dummy variable model to estimate the effect of 16 major cities or provinces on diesel demands. The results show that diesel demands is affected 10 regions on the basis of Seoul.

본 연구는 1998년부터 2015년까지의 16개 시도별 경유수요량, 경유제품 판매가격(유통단계), 및 총 부가가치생산의 패널데이터를 이용하여, 패널GLS, 고정효과(Fixed Effect), 확률효과(Random Effect) 및 동적패널(Dynamic Panel) 모형을 통해 국내 경유수요함수를 추정하고, 이를 통해 가격탄력성과 소득탄력성을 추정하였다. 단기 가격탄력성은 -0.2146(패널GLS), -0.2886(고정효과), -0.2854(확률효과), -0.1905(동적패널)로 추정되었고, 단기 소득탄력성은 0.7379(패널GLS), 0.4119(고정효과), 0.7260(확률효과), 0.4166(동적패널)로 추정되었는데, 모두 비탄력적인 것으로 나타났다. 장기 가격탄력성과 장기 소득탄력성은 동적패널을 통해 추정하였고, 각각 -0.4784, 1.0461로 유의하게 나타났다. 경유 수요는 소득에 증감에 대해 단기적으로는 비탄력적이나, 장기적으로는 탄력적으로 나타나고 있다. 추가로 서울지역을 기준으로 지역변수를 더미변수(Dummy Variables)로 하여 각 지역의 경유수요로의 효과를 검정하였는데, 10개 지역에서 상대적으로 유의하게 추정되었다.

Keywords

References

  1. 나인강., 2006, 기술 발전을 고려한 에너지 수요 추정, Korea Energy Economic Review, Vol. 5, pp. 205-222
  2. 강만옥,. 2007, 최근 수송 부문 에너지 세제 개편의 성과 평가, 한국환경정책평가원
  3. 이승재, 한종호, 유승훈., 2013, 시계열 자료를 이용한 도시가스의 수요함수 추정. Journal of Energy Engineering, Vol. 22, No. 4, pp. 370-375 https://doi.org/10.5855/ENERGY.2013.22.4.370
  4. 이승재, 한종호, 유승훈., 2012, 수송용 LPG 수요 함수의 추정 및 활용, Journal of Energy Engineering, Vol. 21, No. 3, pp. 301-308 https://doi.org/10.5855/ENERGY.2012.21.3.301
  5. Graham, Daniel J., and Stephen Glaister., 2002, The demand for automobile fuel: a survey of elasticities, Journal of Transport Economics and Policy (JTEP), Vol. 36, No. 1, pp. 1-25
  6. Koshal, Rajindar K., et al., 2007, Demand for gasoline in Japan, International Journal of Transport Economics/Rivista internazionale di economia dei trasporti, pp. 351-367
  7. Barla, Philippe, Mathieu Gilbert-Gonthier, and Jean-Rene Tagne Kuelah., 2014, The demand for road diesel in Canada, Energy Economics, Vol. 43, pp. 316-322 https://doi.org/10.1016/j.eneco.2014.03.008
  8. Baltagi, Badi H., and Ping X. Wu., 1999, Unequally spaced panel data regressions with AR (1) disturbances, Econometric Theory, Vol. 15, No. 06, pp. 814-823
  9. 민인식, and 최필선., 2013, STATA 패널데이터 분석, 지필미디어
  10. Agthe, Donald E., and R. Bruce Billings., 1990, Dynamic models of residential water demand, Water Resources Research, Vol. 16 No. 3, pp. 476-480 https://doi.org/10.1029/WR016i003p00476
  11. Arellano, Manuel, and Stephen Bond., 1991, Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, The review of economic studies, Vol. 58 No. 2, pp. 277-297 https://doi.org/10.2307/2297968
  12. Drukker, David M., 2003, Testing for serial correlation in linear panel-data models, Stata Journal, Vol. 3 No. 2, pp. 168-177
  13. Wooldridge, J. M., 2002, Econometric Analysis of Cross Section and Panel Data, Cambridge, MA: MIT Press
  14. Sargan, John D., 1958, The estimation of economic relationships using instrumental variables, Econometrica: Journal of the Econometric Society, pp. 393-415
  15. Hansen, Lars Peter., 1982, Large sample properties of generalized method of moments estimators, Econometrica: Journal of the Econometric Society, pp. 1029-1054
  16. Roodman, David., 2009, A note on the theme of too many instruments, Oxford Bulletin of Economics and statistics, Vol. 71, No. 1, pp. 135-158 https://doi.org/10.1111/j.1468-0084.2008.00542.x