SavedAdd your journal

Journals / Ekoist: Journal of Econometrics and Statistics / 2023 / Sayı: 39

A Comparison of Forecasting Performance of PPML and OLS estimators: The Gravity Model in the Air Cargo Market

Gizem Kaya(ISTANBUL TECHNICAL UNIVERSITY, MANAGEMENT ENGINEERING PR.)0000-0002-6870-7219
Umut Aydın(BANDIRMA ONYEDI EYLUL UNIVERSITY, OMER SEYFETTIN FACULTY OF APPLIED SCIENCES, DEPARTMENT OF INTERNATIONAL TRADE AND LOGISTICS)0000-0003-4802-8793
Burç Ülengin(ISTANBUL TECHNICAL UNIVERSITY, MANAGEMENT ENGINEERING PR.)0000-0001-5276-8861
Pages
112–128
Publication Date
2023-12-27
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

However, the forecast performance of models differs for some cities.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Using international air cargo data from Turkey, this study compares the forecast performance of three different approaches in the air transport literature for the basic gravity model parameter estimation.
📊
Key Findings & Data:The forecast performance of the models developed after estimating the equation with three different approaches was compared with error metrics and the Diebold-Mariano test.
💡
Conclusion & Impact:However, the forecast performance of models differs for some cities.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:The gravity model · Poisson pseudo-maximum-likelihood · Ordinary least squares

Abstract

Using international air cargo data from Turkey, this study compares the forecast performance of three different approaches in the air transport literature for the basic gravity model parameter estimation. The first approach uses ordinary least squares to estimate the gravity model, which is frequently utilized in air transport literature. The second approach, like the first, employs the log-linear estimate technique, but unlike the first, it adds a small amount to the observations with a zero-valued dependent variable and includes them in the analysis. The third method is to estimate the gravity model using the Poisson pseudo maximum-likelihood estimator, which is an alternative to the ordinary least square estimator. The forecast performance of the models developed after estimating the equation with three different approaches was compared with error metrics and the Diebold-Mariano test. As a result of the study, the Poisson pseudo-maximum-likelihood estimator was observed to be the estimator with by far the best forecast performance for the total amount of cargo carried. However, the forecast performance of models differs for some cities.

🔖

Cite This Article

Kaya, G., Aydın, U., Ülengin, B. (2023). A Comparison of Forecasting Performance of PPML and OLS estimators: The Gravity Model in the Air Cargo Market. Ekoist: Journal of Econometrics and Statistics, Sayı: 39, 112–128. https://doi.org/10.26650/ekoist.2023.39.1310639

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (60)

  1. [dataset] TURKSTAT, 2021. Turkish Statistical Institute. Retrieved from: https://biruni.tuik.gov.tr/bolgeselistatistik/sorguSayfa.do?target=degisken (accessed 01 May 2021). google scholar
  2. [dataset] World Bank, 2021. The World Bank. Retrieved from: https://data.worldbank.org/ (accessed 01 May 2021). google scholar
  3. Alekseev, K. P. G., & Seixas, J. M. (2009). A multivariate neural forecasting modeling for air transport-preprocessed by decomposition: a Brazilian application. Journal of Air Transport Management, 15(5), 212-216. google scholar
  4. Alekseev, K. P G., & Seixas, J. M. (2009). A multivariate neural forecasting modeling for air transport-preprocessed by decomposition: a Brazilian application. Journal of Air Transport Management, 15(5), 212-216. google scholar
  5. Alexander, D. W., & Merkert, R. (2017). Challenges to domestic air freight in Australia: Evaluating air traffic markets with gravity modelling. Journal of Air Transport Management, 61, 41-52. google scholar
  6. Alexander, D. W., & Merkert, R. (2021). Applications of gravity models to evaluate and forecast US international air freight markets post-GFC. Transport Policy, 104, 52-62. google scholar
  7. Aydın, U., & Ülengin, B. (2022). Analyzing air cargo flows of Turkish domestic routes: A comparative analysis of gravity models. Journal of Air Transport Management, 102, 102217. google scholar
  8. Baier, S., & Bergstrand, J. H. (2010). Approximating general equilibrium impacts of trade liberalizations using the gravity equation. The Gravity Model in International Trade, 88-134. google scholar
  9. Baker, D., Merkert, R., Kamruzzaman, M., 2015. Regional aviation and economic growth: cointegration and causality analysis in Australia. J. Transport Geogr. 43, 140-150. google scholar
  10. Becker, K., Terekhov, I., & Gollnick, V. (2018). A global gravity model for air passenger demand between city pairs and future interurban air mobility markets identification. In 2018 aviation technology, integration, and operations conference (p. 2885). google scholar
  11. Becker, K., Terekhov, I., Gollnick, V., 2018. A global gravity model for air passenger demand between city pairs and future interurban air mobility markets identification. In 2018 aviation technology, integration, and operations conference pp-2885. google scholar
  12. Ben-Akiva, M., Lerman, S. R., 1985. Discrete Choice Analysis: Theory and Application to travel demand VOL.9. MIT press. google scholar
  13. Blume, H., Daimler-Benz Aerospace AG., 1995. World Market Forecast 1995-2014 for Civil Air Transport, Muenchen, Germany. google scholar
  14. Choi, J. H. 2023. A Study on The Change in The Significance of GDP As a Determinant of Air Demand-Discussions on Brand-New Air Transport Items. Transport Policy, 133, 186-197. google scholar
  15. Cristea, A.D., Hillberry, R., Mattoo, A., 2015. Open skies over the Middle East. The World Economy, 38 (11), 1650-1681. google scholar
  16. Danilov, D. L., 1997. Principal components in time series forecast. Journal of computational and graphical statistics, 6(1), pp. 112-121. google scholar
  17. Desai, J., Srivathsan, S., Lai, W. Y., Li, L., Yu, C., 2023. An optimization-based decision support tool for air cargo loading. Computers & Industrial Engineering, 175, 108816. google scholar
  18. DHMI, 2020. Annual Statistical Yearbook publications of the General Directorate of State Airports Authority, Ankara. google scholar
  19. DHMI, 2021. Aircraft, passenger, freight series and forecast. Retrieved from: https://www.dhmi.gov.tr/Sayfalar/EN/Statistics.aspx. Access Date: 01.06.2021. google scholar
  20. Diebold, F.X. and R.S. Mariano. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13: 253-63. google scholar
  21. Feng, B., Li, Y., & Shen, Z. J. M. (2015). Air cargo operations: Literature review and comparison with practices. Transportation Research Part C: Emerging Technologies, 56, 263-280. google scholar
  22. Fernandes, E., & Pacheco, R. R. (2010). The causal relationship between GDP and domestic air passenger traffic in Brazil. Transportation Planning and Technology, 33(7), 569-581. google scholar
  23. Fildes, R., Wei, Y., & Ismail, S. (2011). Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures. International Journal of Forecasting, 27(3), 902-922. google scholar
  24. Gillen, D., Hazledine, T., 2015. The economics and geography of regional airline services in six countries. Journal of Transport Geography, 46, 129-136. google scholar
  25. Gomez-Herrera, E., 2013. Comparing alternative methods to estimate gravity models of bilateral trade. Empirical economics, 44(3), pp. 1087-1111. google scholar
  26. Grosche, T., Rothlauf, F., Heinzl, A., 2007. Gravity models for airline passenger volume estimation. Journal of Air Transport Management, 13(4), 175-183. google scholar
  27. Grosso, M. G., & Shepherd, B. (2011). Air cargo transport in APEC: Regulation and effects on merchandise trade. Journal of Asian economics, 22(3), 203-212. google scholar
  28. Gül, H., Tatoğlu, F.Y., 2019. Turizm Talebinin Panel Çekim Modeli Çerçevesinde Analizi. Turizm Akademik Dergisi, 6 (1), pp. 49-60. google scholar
  29. Hakim, M. M., & Merkert, R. (2016). The causal relationship between air transport and economic growth: Empirical evidence from South Asia. Journal of Transport geography, 56, 120-127. google scholar
  30. Hazledine, T. (2017). An augmented gravity model for forecasting passenger air traffic on city-pair routes. Journal of Transport Economics and Policy (JTEP), 51(3), 208-224.7 google scholar
  31. Hazledine, T., 2009. Border effects for domestic and international Canadian passenger air travel. Journal of Air Transport Management, 15 (1), 7-13. google scholar
  32. Helpman, E., Melitz, M., & Rubinstein, Y. (2008). Estimating trade flows: Trading partners and trading volumes. The quarterly journal of economics, 123(2), 441-487. google scholar
  33. Hwang, C. C., Shiao, G. C., 2011. Analyzing air cargo flows of international routes: an empirical study of Taiwan Taoyuan International Airport. Journal of Transport Geography, 19(4), pp. 738-744. google scholar
  34. IGA, 2021. Kargo ve Lojistik Merkezi. Retrieved from: https://www.igairport.com/tr/istanbul-havalimani/kargo-ve-lojistik-merkezi. Access Date: 21.06.2021. google scholar
  35. Isard, W., Peck, M. J., 1954. Location theory and international and interregional trade theory. The Quarterly Journal of Economics, pp. 97-114. google scholar
  36. Kanafani, A., 1983. Transportation demand analysis, New York. google scholar
  37. Law, R., Au, N., 1999. A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20(1), pp. 89-97. google scholar
  38. Maddala, G. S. (1983). Methods of estimation for models of markets with bounded price variation. International Economic Review, 361-378. google scholar
  39. Matsumoto, H. (2004). International urban systems and air passenger and cargo flows: some calculations. Journal of Air Transport Management, 10(4), 239-247. google scholar
  40. Matsumoto, H. (2007). International air network structures and air traffic density of world cities. Transportation Research Part E: Logistics and Transportation Review, 43(3), 269-282. google scholar
  41. Matsumoto, H., & Domae, K. (2018). The effects of new international airports and air-freight integrator’s hubs on the mobility of cities in urban hierarchies: A case study in East and Southeast Asia. Journal of Air Transport Management, 71, 160-166. google scholar
  42. Matsumoto, H., & Domae, K. (2019). Assessment of competitive hub status of cities in Europe and Asia from an international air traffic perspective. Journal of Air Transport Management, 78, 88- google scholar
  43. Matsumoto, H., Domae, K., & O’Connor, K. (2016). Business connectivity, air transport and the urban hierarchy: A case study in East Asia. Journal of Transport Geography, 54, 132-139. google scholar
  44. Nam, K., Schaefer, T., 1995. Forecasting international airline passenger traffic using neural networks. The Logistics and Transportation Review, 31(3), pp. 239-252. google scholar
  45. Santos, S., Tenreyro, S., 2006. The log of gravity. Review of Economics and Statistics, 88, pp. 641-658. google scholar
  46. Schneider, H. (1986). Truncated and censored samples from normal populations. Marcel Dekker, Inc. google scholar
  47. Schultz, R. L. (1972). Studies of Airline Passenger Demand: A Review. Transportation Journal, 48-62. google scholar
  48. Sun, S., Lu, H., Tsui, K. L., & Wang, S. (2019). Nonlinear vector auto-regression neural network for forecasting air passenger flow. Journal of Air Transport Management, 78, 54-62. google scholar
  49. Suryani, E., Chou, S. Y., & Chen, C. H. (2010). Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework. Expert Systems with Applications, 37(3), 2324-2339. google scholar
  50. Tatoğlu, F.Y., 2018. İleri panel veri analizi. Basım. İstanbul: Beta Yayıncılık. google scholar
  51. Tratar, L. F., & Strmcnik, E. (2016). The comparison of Holt-Winters method and Multiple regression method: A case study. Energy, 109, 266-276. google scholar
  52. Van Bergeijk, P. A., & Brakman, S. (Eds.). (2010). The gravity model in international trade: Advances and applications. Cambridge University Press. google scholar
  53. Westerlund, J., Wilhelmsson, F., 2011. Estima,ting the gravity model without gravity using panel data. Applied Economics, 43(6), pp. 641-649. google scholar
  54. Wickham, R. R., 1995. Evaluation of forecasting techniques for short-term demand of air transportation. Cambridge, Mass.: Massachusetts Institute of Technology, Dept. of Aeronautics & Astronautics. Flight Transportation Laboratory, Massachusetts Institute of Technology, Massachusetts, USA. google scholar
  55. World Bank, 2023. Air transport, freight. Retrieved from: https://data.worldbank.org/indicator/IS.AIR.GOOD.MT.K1?locations=TR (accessed 10 February 2023) google scholar
  56. Yamaguchi, K., 2008. International trade and air cargo: Analysis of US export and air transport policy. Transportation Research Part E: Logistics and Transportation Review, 44(4), pp. 653-663. google scholar
  57. Zhang, X., Zheng, Y., & Wang, S. (2019). A demand forecasting method based on stochastic frontier analysis and model average: An application in air travel demand forecasting. Journal of Systems Science and Complexity, 32(2), 615-633. google scholar
  58. Zhang, Y., Findlay, C., 2014. Air transport policy and its impacts on passenger traffic and tourist flows. Journal of Air Transport Management, 34, 42-48. google scholar
  59. Zhang, Y., Zhang, A., 2016. Determinants of air passenger flows in China and gravity model: Deregulation, LCC and high-speed rail. Jspeed rail. Journal of Transport Economics and ournal of Transport Economics and Policy, 50 (3), 287(3), 287-303.303. google scholar
  60. Schneider, H. (1986). Truncated and censored samples from normal populations. Marcel Dekker, Inc. google scholar