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Spillovers in space and time: where spatial econometrics and Global VAR models meet

Author

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  • Elhorst, J. Paul
  • Gross, Marco
  • Tereanu, Eugen

Abstract

We bring together the spatial and global vector autoregressive (GVAR) classes of econometric models by providing a detailed methodological review of where they meet in terms of structure, interpretation, and estimation methods. We discuss the structure of cross-section connectivity (weight) matrices used by these models and its implications for estimation. Primarily motivated by the continuously expanding literature on spillovers, we define a broad and measurable concept of spillovers. We formalize it analytically through the indirect effects used in the spatial literature and impulse responses used in the GVAR literature. Finally, we propose a practical step-by-step approach for applied researchers who need to account for the existence and strength of cross-sectional dependence in the data. This approach aims to support the selection of the appropriate modeling and estimation method and of choices that represent empirical spillovers in a clear and interpretable form. JEL Classification: C33, C38, C51

Suggested Citation

  • Elhorst, J. Paul & Gross, Marco & Tereanu, Eugen, 2018. "Spillovers in space and time: where spatial econometrics and Global VAR models meet," Working Paper Series 2134, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20182134
    Note: 3098116
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    References listed on IDEAS

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    1. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2016. "Exponent of Cross‐Sectional Dependence: Estimation and Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 929-960, September.
    2. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2015. "Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients," Working Papers 749, Queen Mary University of London, School of Economics and Finance.
    3. Bailey, Natalia & Pesaran, M. Hashem & Smith, L. Vanessa, 2019. "A multiple testing approach to the regularisation of large sample correlation matrices," Journal of Econometrics, Elsevier, vol. 208(2), pages 507-534.
    4. Donald W. K. Andrews, 2005. "Cross-Section Regression with Common Shocks," Econometrica, Econometric Society, vol. 73(5), pages 1551-1585, September.
    5. Graciela L. Kaminsky & Carmen M. Reinhart & Carlos A. Végh, 2003. "The Unholy Trinity of Financial Contagion," Journal of Economic Perspectives, American Economic Association, vol. 17(4), pages 51-74, Fall.
    6. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    7. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
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    Cited by:

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    2. Elhorst, J. Paul & Madre, Jean-Loup & Pirotte, Alain, 2020. "Car traffic, habit persistence, cross-sectional dependence, and spatial heterogeneity: New insights using French departmental data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 614-632.
    3. Capasso, Salvatore & D'Uva, Marcella & Fiorelli, Cristiana & Napolitano, Oreste, 2023. "Cross-border Italian sovereign risk transmission in EMU countries," Economic Modelling, Elsevier, vol. 126(C).
    4. Fabio Milani, 2021. "COVID-19 outbreak, social response, and early economic effects: a global VAR analysis of cross-country interdependencies," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 223-252, January.
    5. Alicja Olejnik & Agata Zoltaszek, 2020. "Tracing The Spatial Patterns Of Innovation Determinants In Regional Economic Performance," Lodz Economics Working Papers 2/2020, University of Lodz, Faculty of Economics and Sociology.
    6. Margaretic, Paula & Cifuentes, Rodrigo & Carreño, José Gabriel, 2021. "Banks’ interconnections and peer effects: Evidence from Chile," Research in International Business and Finance, Elsevier, vol. 58(C).
    7. Sophie Béreau & Nicolas Debarsy & Cyrille Dossougoin & Jean-Yves Gnabo, 2022. "Contagion in the Banking Industry: a Robust-to-Endogeneity Analysis," Working Papers halshs-03513049, HAL.
    8. Sona Benecka & Ludmila Fadejeva & Martin Feldkircher, 2018. "Spillovers from Euro Area Monetary Policy: A Focus on Emerging Europe," Working Papers 2018/04, Latvijas Banka.
    9. Rubén Ferrer Velasco & Margret Köthke & Melvin Lippe & Sven Günter, 2020. "Scale and context dependency of deforestation drivers: Insights from spatial econometrics in the tropics," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-32, January.
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    11. Hanen Ragoubi & Zouheir Mighri, 2021. "Spillover effects of trade openness on CO2 emissions in middle‐income countries: A spatial panel data approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(3), pages 835-877, June.
    12. You Zheng & Jianzhong Xiao & Jinhua Cheng, 2020. "Industrial Structure Adjustment and Regional Green Development from the Perspective of Mineral Resource Security," IJERPH, MDPI, vol. 17(19), pages 1-18, September.
    13. Ehlert, Andree, 2021. "The socio-economic determinants of COVID-19: A spatial analysis of German county level data," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    14. Gefang, Deborah & Hall, Stephen G. & Tavlas, George S. & Wang, Yongli, 2024. "Quantifying spillovers among regions," Journal of International Money and Finance, Elsevier, vol. 140(C).
    15. Benecká, Soňa & Fadejeva, Ludmila & Feldkircher, Martin, 2020. "The impact of euro Area monetary policy on Central and Eastern Europe," Journal of Policy Modeling, Elsevier, vol. 42(6), pages 1310-1333.

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    More about this item

    Keywords

    GVARs; spatial models; spillovers; weak and strong cross-sectional dependence;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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