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Spatial panel data models with common shocks

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  • Bai, Jushan
  • Li, Kunpeng

Abstract

Spatial effects and common-shocks effects are of increasing empirical importance. Each type of effect has been analyzed separately in a growing literature. This paper considers a joint modeling of both types. Joint modeling allows one to determine whether one or both of these effects are present. A large number of incidental parameters exist under the joint modeling. The quasi maximum likelihood method (MLE) is proposed to estimate the model. Heteroskedasticity is explicitly estimated. This paper demonstrates that the quasi-MLE is effective in dealing with the incidental parameters problem. An inferential theory including consistency, rate of convergence and limiting distributions is developed. The quasi-MLE can be easily implemented via the EM algorithm, as confirmed by the Monte Carlo simulations. The simulations further reveal the excellent finite sample properties of the quasi-MLE. Some extensions are discussed.

Suggested Citation

  • Bai, Jushan & Li, Kunpeng, 2013. "Spatial panel data models with common shocks," MPRA Paper 52786, University Library of Munich, Germany, revised 09 Mar 2014.
    • Handle: RePEc:pra:mprapa:52786
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    References listed on IDEAS

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    3. Bai, Jushan & Li, Kunpeng, 2021. "Dynamic spatial panel data models with common shocks," Journal of Econometrics, Elsevier, vol. 224(1), pages 134-160.
    4. Lina Lu, 2017. "Simultaneous Spatial Panel Data Models with Common Shocks," Supervisory Research and Analysis Working Papers RPA 17-3, Federal Reserve Bank of Boston.
    5. Ma, Shujie & Su, Liangjun, 2018. "Estimation of large dimensional factor models with an unknown number of breaks," Journal of Econometrics, Elsevier, vol. 207(1), pages 1-29.
    6. Cynthia Fan Yang, 2021. "Common factors and spatial dependence: an application to US house prices," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 14-50, January.
    7. Georg Keilbar & Juan M. Rodriguez-Poo & Alexandra Soberon & Weining Wang, 2022. "A semiparametric approach for interactive fixed effects panel data models," Papers 2201.11482, arXiv.org, revised Mar 2023.
    8. Li, Yue & Wang, Mingshu & Zhao, Qunshan, 2023. "Understanding Urban Traffic Flows in Response to COVID-19 Pandemic with Emerging Urban Big Data in Glasgow," OSF Preprints kwbdz, Center for Open Science.
    9. 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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    More about this item

    Keywords

    Panel data models; spatial interactions; common shocks; cross-sectional dependence; incidental parameters; maximum likelihood estimation;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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