패널회귀분석을 활용한 항공화물의 가격 및 소득 탄력도 분석 연구
Study on Price and Income Elasticity of Air Cargo using Panel Regression Analysis
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Airports, as a major facility for handling high-value import and export cargo, have played an important role in developing Korea’s import and export industry. The number of commodities imported and exported through airports is about 199 based on 3 units of MTI, and as of 2020, the air cargo volume is about 1,117,086 tons. The purpose of this study is to analyze how the volume of air import and export cargo changes over time through price and income elasticity analysis. In this study, panel regression analysis was performed with monthly data from January 2011 to December 2020 for 49 items, and elasticity analysis was performed using a generalized least squares model, a fixed-effect model, and a random effects model. As a result of the analysis, it was found that the price elasticity of air export cargo (-0.525∼-0.623) is higher than that of air import cargo (-0.354∼-0.435). In addition, the air cargo volume in 2020, when COVID-19 broke out, decreased compared to other years, and it was analyzed that air import cargo was more affected by COVID-19 than air export cargo. The limitation of this study is that in the case of air transport rates, which are the main explanatory variable that determines price elasticity, different conditions were applied to prices. In future studies, it is considered that additional research that equates price conditions will be necessary.
Abstract
Airports, as a major facility for handling high-value import and export cargo, have played an important role in developing Korea’s import and export industry. The number of commodities imported and exported through airports is about 199 based on 3 units of MTI, and as of 2020, the air cargo volume is about 1,117,086 tons. The purpose of this study is to analyze how the volume of air import and export cargo changes over time through price and income elasticity analysis. In this study, panel regression analysis was performed with monthly data from January 2011 to December 2020 for 49 items, and elasticity analysis was performed using a generalized least squares model, a fixed-effect model, and a random effects model. As a result of the analysis, it was found that the price elasticity of air export cargo (-0.525∼-0.623) is higher than that of air import cargo (-0.354∼-0.435). In addition, the air cargo volume in 2020, when COVID-19 broke out, decreased compared to other years, and it was analyzed that air import cargo was more affected by COVID-19 than air export cargo. The limitation of this study is that in the case of air transport rates, which are the main explanatory variable that determines price elasticity, different conditions were applied to prices. In future studies, it is considered that additional research that equates price conditions will be necessary.
참고문헌 (34)
- 권남호, "도구변수의 활용 : 정책학⋅행정학연구를 중심으로", 현대사회와 행정, 28(3), pp.33~62 (2018)
- 김 석, "패널회귀분석을 이용한 내항 화물운송사업체의 경영특성 분석에 관한 연구", Journal of Digital Convergence, 17(3), pp.79~92 (2019)
- 김창범, "고기술집중도, 전자상거래 규모, 해외직접투자가 한국의 항공수출에 미치는 영향 : 패널분석기법의 적용", 해운물류연구, 34(3), pp.431~445 (2018)
- 류나현, "다중선형회귀모형에서의변수선택기법 평가", Journal of the Korean Institute of Industrial Engineers, pp.314~326 (2016)
- 민인식, "기초통계와 회귀분석", 한국 STATA학회, 2020, pp.216~232 (2020)
- 박용남, "항공-해운 간 전환 가능한화물품목에 관한 연구", 로지스틱스 연구, 31(2), pp.1~15 (2023)
- 박진희, "전자상거래 환경을 고려한 위계별물류시설의 최적 입지 선정에 관한 연구-예측배송시스템 관점에서", 로지스틱스 연구, 24(4), pp.41~58 (2016)
- 손재철, "인천공항 항공화물 주요품목 분석 : 중력모형을 중심으로", 로지스틱스 연구, 29(5), pp.1~18 (2021)
- 오재균, "패널회귀분석을 이용한 글로벌 선사의 재무요인 특성분석에 관한 연구", Journal of Digital Convergence, 17(4), pp.65~73 (2019)
- 안영균, "품목별 해상 물동량이 세계경제에 미치는 영향 요인 분석", 무역학회지, 42(2), pp.277~296 (2017)
- 여기태, "패널회귀분석을 이용한 연안여객선 취항계획특성분석에 관한연구", 해운물류연구, 35(1), pp.21~50 (2019)
- 정성봉, "철도화물수송의 환적효과분석 연구", 로지스틱스 연구, 19(1), pp.159~168 (2011)
- 코리아쉬핑가제트, "상반기 항공운송, LCD 물량 해상전환에 시황 ‘급랭’", ~ (2006)
- Arlot, S., "A survey of crossvalidation procedures for model selection", Statistics Surveys, 4, pp.40~79 (2010)
- Bertsimas, D., "Algorithmic prediction of health-care costs", Operations Research, 56(6), pp.1382~1392 (2008)
- Cleveland, R. B., "STL : A Seasonal-Trend Decomposition", Journal of Official Statistics, 6(1), pp.3~73 (1990)
- De Livera, A., "Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing", Journal of the American Statistical Association, 106(496), pp.1513~1527 (2011)
- Gelman, A., "Bayesian Data Analysis", pp.2013, Chapman and Hall
- Hughes, J., "The Philosophy of Social Research" (1979), Longman
- Hyndman, R. J., "Forecasting: Principles and Practice" (2018), OTexts
- Automatic time series for forecasting: the forecast package for R (No. 6, "07)", Hyndman, R. J., 2007
- Hyndman, R. J., "Another look at forecast-accuracy metrics for intermittent demand", Foresight : The International Journal of Applied Forecasting, 4(4), pp.43~46 (2006)
- Hyndman, R. J., "A state space framework for automatic forecasting using exponential smoothing methods", International Journal of Forecasting, 18(3), pp.439~454 (2002)
- James, G., "An Introduction to Statistics Learning", pp.112~ (2013), Sringer
- McAfee, A., "Big data : The management revolution", Harvard Business Review, 90(10), pp.60~68 (2012)
- McElreath, R., "Statistical Rethinking: A Bayesian course with examples in R and Stan, Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany. (Texts in statistical science; 122)", pp.2016, CRC Press
- Mehmet Mehmetoglu, "Structural Equation Modeling with Partial Least Squares using STATA and R", pp.162~163 (2021)
- Harvey, A. C., "Handbook of Statistics. Vol. 11", pp.261~302 (1993), Elsevier
- Ross, S. M., "Introduction to Probability and Statistic for Engineers and Scientists" (2004), Academic Press
- Terwiesch, C., "Matching supply with demand: An introduction to operations management" (2012), Mcgraw-Hill Education-Europe
- Venkatesh, K. V., "Cash demand forecasting in ATMs by clustering and neural networks", European Journal of Operational Research, 232(2), pp.383~392 (2014)
- Wang, G. H. K., "Functional Forms and Aggregate U.S. Domestic Air Cargo Demand:1950-1977", Transportation Res, 15A, pp.249~256
- Yang, J., "Feature subset selection using genetic algorithm", IEEE Intelligent Systems and Their Applications, 13(2), pp.44~49 (1998)
- Yao, S., "Air transport and regional economic growth in China", Asia-Pacific Journal of Accounting & Economics, 19(3), pp.318~329 (2012)