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A multivariate cointegrating vector auto regressive model of freight transport demand: evidence from Indian railways

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  • Kulshreshtha, Mudit
  • Nag, Barnali
  • Kulshrestha, Mukul

Abstract

In this paper long run structural relationship for freight transport demand is derived for railways in India using annual time series data for 1960-1995. Some of the recent developments in multivariate dynamic econometric time series modelling have been employed such as estimation of long-run structural cointegrating relationship, short-run dynamics and measurement of the effects of shocks and their persistence during the evolution of dynamic freight transport demand system. The models are estimated using a cointegrating vector autoregressive (VAR) modelling framework, which allows for endogeneity of regressors. Results indicate high GDP elasticity and low price elasticity, with real freight rate, i.e. the price variable behaving exogenously with respect to the system. Any disequilibrium in the short-run is likely to be corrected in the long run via adjustments in freight transport demand and GDP. Further, the demand system seems to be stable in the long run and converges to equilibrium in a period of around 3 years after a typical system-wide shock.

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  • Kulshreshtha, Mudit & Nag, Barnali & Kulshrestha, Mukul, 2001. "A multivariate cointegrating vector auto regressive model of freight transport demand: evidence from Indian railways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 29-45, January.
    • Handle: RePEc:eee:transa:v:35:y:2001:i:1:p:29-45
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    3. Shahbaz, Muhammad & Khraief, Naceur & Jemaa, Mohamed Mekki Ben, 2015. "On the causal nexus of road transport CO2 emissions and macroeconomic variables in Tunisia: Evidence from combined cointegration tests," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 89-100.
    4. Pradhan, Rudra P. & Bagchi, Tapan P., 2013. "Effect of transportation infrastructure on economic growth in India: The VECM approach," Research in Transportation Economics, Elsevier, vol. 38(1), pages 139-148.
    5. Janusz Myszczyszyn & Bartosz Mickiewicz, 2019. "Long-Term Correlations between the Development of Rail Transport and the Economic Growth of the German Reich (1872-1913)," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 126-139.
    6. Cheng, Yung-Hsiang & Chang, Yu-Hern & Lu, I.J., 2015. "Urban transportation energy and carbon dioxide emission reduction strategies," Applied Energy, Elsevier, vol. 157(C), pages 953-973.
    7. Prashant Premkumar & P. N. Ram Kumar, 2019. "Literature Review of Locomotive Assignment Problem from Service Operations Perspective: The Case of Indian Railways," IIM Kozhikode Society & Management Review, , vol. 8(1), pages 74-86, January.
    8. Julien Brunel, 2005. "Le transport de marchandises et la croissance économique," Post-Print halshs-00095907, HAL.
    9. Ching-Chih Chou & Chien-Wen Shen & Dapeng Gao & Yang Gao & Kai Wang & Sang-Bing Tsai, 2018. "Modelling the Dynamic Impacts of High Speed Rail Operation on Regional Public Transport—From the Perspective of Energy Economy," Energies, MDPI, vol. 11(5), pages 1-15, May.
    10. Bhattacharyya, Subhes C. & Timilsina, Govinda R., 2010. "Modelling energy demand of developing countries: Are the specific features adequately captured?," Energy Policy, Elsevier, vol. 38(4), pages 1979-1990, April.
    11. Andrii Galkin & Tibor Schlosser & Ivan Cardenas & Dominika Hodakova & Silvia Capayova, 2021. "Freight Demand and Supply Assessment for Implementation of Crowdsourcing Technology: A Case Study in Bratislava, Slovakia," Sustainability, MDPI, vol. 13(7), pages 1-22, March.
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