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Cross-sectional dependence model specifications in a static trade panel data setting

Author

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  • LeSage, James
  • Fischer, Manfred M.

Abstract

The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific and time-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.

Suggested Citation

  • LeSage, James & Fischer, Manfred M., 2019. "Cross-sectional dependence model specifications in a static trade panel data setting," Working Papers in Regional Science 2019/03, WU Vienna University of Economics and Business.
    • Handle: RePEc:wiw:wus046:6886
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    Cited by:

    1. Michael Lebacher & Paul W. Thurner & Göran Kauermann, 2021. "Censored regression for modelling small arms trade volumes and its ‘Forensic’ use for exploring unreported trades," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 909-933, August.
    2. Zhang, Zhenhua & Zhao, Mingcheng & Chen, Yongxi & Song, Melody C. & Gao, Yue & Feng, Yanchao, 2025. "The nexus between energy legislation, energy transition, and energy resilience: Evidence from 55 countries worldwide," Energy, Elsevier, vol. 324(C).
    3. Yang, Zixin & Song, Xiaojun & Yu, Jihai, 2025. "Estimation of spatial autoregressive panel data models with nonparametric endogenous effect," Journal of Econometrics, Elsevier, vol. 252(PA).
    4. Manfred M. Fischer & James P. LeSage, 2020. "Network dependence in multi-indexed data on international trade flows," Journal of Spatial Econometrics, Springer, vol. 1(1), pages 1-26, December.
    5. Yuxue Sheng & James Paul LeSage, 2021. "Interpreting spatial regression models with multiplicative interaction explanatory variables," Journal of Geographical Systems, Springer, vol. 23(3), pages 333-360, July.
    6. Olga Demidova, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    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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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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