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Forecasting spatially dependent origin and destination commodity flows

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  • James LeSage
  • Carlos Llano-Verduras

Abstract

We explore origin–destination forecasting of commodity flows between 15 Spanish regions, using data covering the period from 1995 to 2004. The 1-year-ahead forecasts are based on a recently introduced spatial autoregressive variant of the traditional gravity model. Gravity (or spatial interaction models) attempt to explain variation in $$N=n^2$$ N = n 2 flows between $$n$$ n origin and destination regions that reflect a vector arising from an $$n$$ n by $$n$$ n flow matrix. The spatial autoregressive variant of the gravity model used here takes into account spatial dependence between flows from regions neighboring both the origin and destinations during estimation and forecasting. One-year-ahead forecast accuracy of non-spatial and spatial models are compared. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • James LeSage & Carlos Llano-Verduras, 2014. "Forecasting spatially dependent origin and destination commodity flows," Empirical Economics, Springer, vol. 47(4), pages 1543-1562, December.
  • Handle: RePEc:spr:empeco:v:47:y:2014:i:4:p:1543-1562
    DOI: 10.1007/s00181-013-0786-2
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    Cited by:

    1. Carlos Llano & Santiago Pérez-Balsalobre & Julian Pérez-García, 2018. "Greenhouse Gas Emissions from Intra-National Freight Transport: Measurement and Scenarios for Greater Sustainability in Spain," Sustainability, MDPI, vol. 10(7), pages 1-33, July.
    2. Qingqing Ye & Jiwei Li & Xuesong Kong & Shaokai Zhang, 2021. "Identification and Optimization of the Spatial Structure of Urban and Rural Settlements from a Hierarchical Network Perspective," Land, MDPI, vol. 10(11), pages 1-21, November.
    3. Yin, Zhonghua & Wang, Fang & Gan, Jianbang, 2020. "Spatial spillover effects of global forest product trade," Forest Policy and Economics, Elsevier, vol. 113(C).
    4. Ni, Linglin & Wang, Xiaokun (Cara) & Zhang, Dapeng, 2016. "Impacts of information technology and urbanization on less-than-truckload freight flows in China: An analysis considering spatial effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 12-25.
    5. James P. LeSage & Christine Thomas-Agnan, 2015. "Interpreting Spatial Econometric Origin-Destination Flow Models," Journal of Regional Science, Wiley Blackwell, vol. 55(2), pages 188-208, March.
    6. Kerkman, Kasper & Martens, Karel & Meurs, Henk, 2018. "Predicting travel flows with spatially explicit aggregate models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 68-88.
    7. Moura, Ticiana Grecco Zanon & Chen, Zhangliang & Garcia-Alonso, Lorena, 2019. "Spatial interaction effects on inland distribution of maritime flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 128(C), pages 1-10.

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    More about this item

    Keywords

    Gravity models; Bayesian spatial autoregressive regression model; Spatial connectivity of origin–destination flows; C11; C23; O47; O52;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe

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