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A Bayesian spatial panel model with heterogeneous coefficients

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  • LeSage, James P.
  • Chih, Yao-Yu

Abstract

We extend the heterogeneous coefficients spatial autoregressive panel model from Aquaro et al. (2015) to allow for Bayesian prior information. A Markov Chain Monte Carlo estimation methodology is set forth for the Bayesian model. Monte Carlo performance results mirror those from quasi maximum likelihood estimation set forth in Aquaro et al. (2015). Matrix expressions for marginal effects used to interpret these models are set forth. The heterogeneous coefficients spatial autoregressive panel model is capable of producing estimates of spillin and spillout effects for each region in the sample. Spillin effects reflect the impact of changes in neighboring region characteristics on own-region outcomes, while spillout effects show how changes in own-region characteristics impact neighboring region outcomes. We illustrate the model using a panel wage curve relationship for the contiguous US states over the 67 months from January 2011 to July 2016.

Suggested Citation

  • LeSage, James P. & Chih, Yao-Yu, 2018. "A Bayesian spatial panel model with heterogeneous coefficients," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 58-73.
    • Handle: RePEc:eee:regeco:v:72:y:2018:i:c:p:58-73
    DOI: 10.1016/j.regsciurbeco.2017.02.007
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    1. 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.
    2. Peter Nijkamp & Jacques Poot, 2005. "The Last Word on the Wage Curve?," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 421-450, July.
    3. Corinne Autant-Bernard & James P. LeSage, 2019. "A heterogeneous coefficient approach to the knowledge production function," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(2), pages 196-218, April.
    4. James P. LeSage & R. Kelley Pace, 2014. "The Biggest Myth in Spatial Econometrics," Econometrics, MDPI, vol. 2(4), pages 1-33, December.
    5. LeSage, James P. & Chih, Yao-Yu, 2016. "Interpreting heterogeneous coefficient spatial autoregressive panel models," Economics Letters, Elsevier, vol. 142(C), pages 1-5.
    6. Holloway, Garth & Shankar, Bhavani & Rahman, Sanzidur, 2002. "Bayesian spatial probit estimation: a primer and an application to HYV rice adoption," Agricultural Economics, Blackwell, vol. 27(3), pages 383-402, November.
    7. J. Paul Elhorst & Uwe Blien & Katja Wolf, 2007. "New Evidence on the Wage Curve," International Regional Science Review, , vol. 30(2), pages 173-191, April.
    8. Lee, Lung-fei & Yu, Jihai, 2010. "A Spatial Dynamic Panel Data Model With Both Time And Individual Fixed Effects," Econometric Theory, Cambridge University Press, vol. 26(2), pages 564-597, April.
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    Cited by:

    1. Junyue Wu & Yasumasa Matsuda, 2021. "A threshold extension of spatial dynamic panel model with fixed effects," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-30, December.
    2. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2021. "Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 18-44, January.
    3. Gianfranco Piras & Mauricio Sarrias, 2023. "Heterogeneous spatial models in R: spatial regimes models," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-32, December.
    4. Tamás Krisztin & Philipp Piribauer, 2021. "A Bayesian spatial autoregressive logit model with an empirical application to European regional FDI flows," Empirical Economics, Springer, vol. 61(1), pages 231-257, July.
    5. 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).
    6. Hou, Zhezhi & Jin, Man & Kumbhakar, Subal C., 2020. "Productivity spillovers and human capital: A semiparametric varying coefficient approach," European Journal of Operational Research, Elsevier, vol. 287(1), pages 317-330.
    7. Debarsy, Nicolas & Dossougoin, Cyrille & Ertur, Cem & Gnabo, Jean-Yves, 2018. "Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach," Journal of Economic Dynamics and Control, Elsevier, vol. 87(C), pages 21-45.
    8. Alberto Gude & Inmaculada Álvarez & Luis Orea, 2018. "Heterogeneous spillovers among Spanish provinces: a generalized spatial stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 50(3), pages 155-173, December.
    9. Huang, Danyang & Hu, Wei & Jing, Bingyi & Zhang, Bo, 2023. "Grouped spatial autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    10. Tamás Krisztin & Philipp Piribauer & Michael Wögerer, 2020. "The spatial econometrics of the coronavirus pandemic," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 209-218, December.
    11. Orea, Luis & Álvarez, Inmaculada C., 2019. "Spatial Production Economics," Efficiency Series Papers 2019/06, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    12. Mark J. Holmes & Jesús Otero, 2022. "The wage curve within and across regions: new insights from a pairwise view of US states," Empirical Economics, Springer, vol. 62(5), pages 2069-2089, May.
    13. Debarsy, Nicolas & Yang, Zhenlin, 2018. "Editorial for the special issue entitled: New advances in spatial econometrics: Interactions matter," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 1-5.
    14. Chen, Jia & Shin, Yongcheol & Zheng, Chaowen, 2022. "Estimation and inference in heterogeneous spatial panels with a multifactor error structure," Journal of Econometrics, Elsevier, vol. 229(1), pages 55-79.

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