All Issue

2018 Vol.36, Issue 2 Preview Page
April 2018. pp. 141-154
지속적으로 증가하는 국제선 항공수요에 대웅하기 위해 지방 광역권에도 새로운 공항 건설 및 기존 공항 확장 계획이 이루어지고 있다. 그러나 기존 항공수요예측은 우리나라 전체 항공수요 또는 주요 도시 간의 항공수요에 대해서 수행되어 왔으며, 지방의 고유 특성을 고려한 지역별 항공수요예측은 많이 이루어지지 않았다. 본 연구에서는 영남권 국제선 항공수요를 대상으로 하였고, 현실적으로 관측하기 어려운 지방 광역권의 고유 특성을 반영할 수 있는 패널 자료를 활용한 fixed-effects model을 최적 모형으로 제안하였다. 모형 검증결과를 살펴보면 패널 자료 분석은 시계열 특성을 가지는 몇 개의 거시 사회경제지표만을 사용한 모형에서 다루기 어려운 허구적 회귀와 미관찰 이질성을 효과적으로 처리하고 있음을 알 수 있다. 다양한 통계적 검증과 적합성 평가를 통해서 본 연구에서 제안한 fixed-effects model이 다른 계량경제 모형들에 비해서 영남권 국제선 수요예측에 있어서 우수함을 증명하였다.
In order to meet the ever-increasing demand for international air travel, several plans are underway to open new airports and expand existing provincial airports. However, existing air demand forecasts have been based on the total air demand in Korea or the air demand among major cities. There is not much forecast of regional air demand considering local characteristics. In this study, the outbound air travel demand in the southeastern region of Korea was analyzed and the fixed-effects model using panel data was proposed as an optimal model that can reflect the inherent characteristics of metropolitan areas which are difficult to observe in reality. The results of model validation show that panel data analysis effectively addresses the spurious regression and unobserved heterogeneity that are difficult to handle in a model using only a few macroeconomic indicators with time series characteristics. Various statistical validation and conformance tests suggest that the fixed-effects model proposed in this study is superior to other econometric models in predicting demand for international demand in the southeastern region.
  1. Abed S. Y., Ba-Fail A. O., Jasimuddin M. (2001), An Econometric Analysis of International Air Travel in Saudi Arabia, Journal of Air Transport Management, 7, 143-148.10.1016/S0969-6997(00)00043-0
  2. Ashenfelter O., Levine P. B., Zimmerman D. J. (2003), Statistics and Econometrics: Methods and Applications, John Wiley & Sons Inc., New Jersey, U.S.A.
  3. Card D. (1990), The Impact of the Mariel Boatlift on the Miami Labor Market, Industrial and Labor Relations Review, 43, 245-257.10.2307/252370210.1177/001979399004300205
  4. Carson R. T., Cenesizoglu T., Parker R. (2011), Forecasting (aggregate) Demand for US Commercial Air Travel, International Journal of Forecasting, 27, 923-941.10.1016/j.ijforecast.2010.02.010
  5. DellaVigna S., Kaplan E. (2007), The Fox News Effect: Media Bias and Voting, Quarterly Journal of Economics, 22 (3), 1187-1234.10.1162/qjec.122.3.1187
  6. Freeman R. B. (1984), Longitudinal Analyses of the Effect of Trade Unions, Journal of Labor Economics, 3, 1-26.10.1086/298021
  7. Jorge-Calderon J. D. (1997), A Demand Model for Scheduled Airline Services on International European Routes, Journal of Air Transport Management, 3(1), 23-35.10.1016/S0969-6997(97)82789-5
  8. Kanafani A. (1983), Transportation Demand Analysis, McGraw-Hill, New York, U.S.A.
  9. Karlaftis M. G., Zografos K. G., Papastavrou J. D., Charnes J. M. (1996), Methodological Framework for Air-Travel Demand Forecasting, Journal of Transportation Engineering, 122(2), 96-104.10.1061/(ASCE)0733-947X(1996)122:2(96)
  10. Lim C., McAleer M. (2002), Time Series Forecasts of International Travel Demand for Australia, Tourism Management, 23, 389-396.10.1016/S0261-5177(01)00098-X
  11. Park J., Kim B. J., Kim W., Jang E. (2016), The Development of Econometric Model for Air Transportation Demand Based on Stationarity in Time-series, J. Korean Soc. Transp., 34(1), Korean Society of Transportation, 95-106.10.7470/jkst.2016.34.1.095
  12. Rengaraju V. R., Arasan V. (1992), Modelling for Air Travel Demand, Journal of Transportation Engineering, 118(3), 371-380.10.1061/(ASCE)0733-947X(1992)118:3(371)
  13. Stock J. H., Watson M. W. (2007), Introduction to Econometrics, Pearson Education Inc., London, U.K.
  14. Valdes V. (2015), Determinants of Air Travel Demand in Middle Income Countries, Journal of Air Transport Management, 42, 75-84.10.1016/j.jairtraman.2014.09.002
  15. Xiao Y., Liu J. J., Hu Y., Wang Y., Lai K. K., Wang S. (2014), A Neuro-fuzzy Combination Model Based on Singular Spectrum Analysis for Air Transport Demand Forecasting, Journal of Air Transport Management, 39, 1-11.10.1016/j.jairtraman.2014.03.004
Journal Informaiton Agriculture and Life Sciences Research Institute Journal of Korean Society of Transportation
  • NRF
  • crosscheck
  • crossref crossmark
  • crossref cited-by
  • crossref funder-registry
  • orcid
  • open access
Journal Informaiton Journal Informaiton - close