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Estimation and Inference in Heterogeneous Spatial Panel Data Models with a Multifactor Error Structure

Author

Listed:
  • Jia Chen
  • Yongcheol Shin
  • Chaowen Zheng

Abstract

In this paper, we develop a unifying econometric framework for the analysis of heterogeneous panel data models that can account for both spatial dependence and unobserved common factors. To tackle the challenging issues of endogeneity caused by both the spatial lagged term and the correlation between regressors and factors, we propose to approximate common factors by cross-section averages of independent variables only, and deal with the spatial endogeneity via the instrumental variables. We develop the individual estimators as well as the Mean Group and the Pooled estimators, and establish their consistency and asymptotic normality. Monte Carlo simulations confirm that the finite sample performance of our proposed estimators are quite satisfactory. We demonstrate the usefulness of our approach with an application to a gravity model of bilateral trade flows for 91 pairs of 14 European Union (EU) countries, and find that the trade flows between the UK and EU members would fall substantially following a hard Brexit.

Suggested Citation

  • Jia Chen & Yongcheol Shin & Chaowen Zheng, 2020. "Estimation and Inference in Heterogeneous Spatial Panel Data Models with a Multifactor Error Structure," Discussion Papers 20/03, Department of Economics, University of York.
    • Handle: RePEc:yor:yorken:20/03
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    References listed on IDEAS

    as
    1. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    2. Kapetanios, G. & Pesaran, M. Hashem & Yamagata, T., 2011. "Panels with non-stationary multifactor error structures," Journal of Econometrics, Elsevier, vol. 160(2), pages 326-348, February.
    3. 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.
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    Cited by:

    1. Jin Seo Cho & Matthew Greenwood‐Nimmo & Yongcheol Shin, 2023. "Recent developments of the autoregressive distributed lag modelling framework," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 7-32, February.

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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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