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A threshold extension of spatial dynamic panel model with fixed effects

Author

Listed:
  • Junyue Wu

    (Tohoku University)

  • Yasumasa Matsuda

    (Tohoku University)

Abstract

This paper proposes a threshold extension of the spatial dynamic panel data (SDPD) model with fixed effects. We introduce a threshold variable to account for regional dependencies of parameters in SDPD models. Moreover, we applied an extension of Yang (J Econom 205(2):423–447, 2018) proposed unified M-estimation to estimate the parameters in the threshold SDPD models, where the consistency and asymptotic normality are established theoretically when the number of cross-sectional units tends to be infinite. The M-estimation is compared with the conditional quasi-maximum likelihood estimation by Monte Carlo experiments, showing that the M-estimation yields an estimation of less bias in cases of short time panels with robust standard errors under non-normality. We illustrate an empirical application of the threshold SDPD model to the U.S. state-level GDP and power usage growth data from 1998 to 2018, detecting the non-trivial regional dependencies of SDPD model parameters.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jospat:v:2:y:2021:i:1:d:10.1007_s43071-021-00008-1
    DOI: 10.1007/s43071-021-00008-1
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    References listed on IDEAS

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    1. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, 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. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    4. 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.
    5. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    6. Fallahi, Firouz, 2011. "Causal relationship between energy consumption (EC) and GDP: A Markov-switching (MS) causality," Energy, Elsevier, vol. 36(7), pages 4165-4170.
    7. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    8. Yang, Zhenlin, 2018. "Unified M-estimation of fixed-effects spatial dynamic models with short panels," Journal of Econometrics, Elsevier, vol. 205(2), pages 423-447.
    9. Mahalingam, Brinda & Orman, Wafa Hakim, 2018. "GDP and energy consumption: A panel analysis of the US," Applied Energy, Elsevier, vol. 213(C), pages 208-218.
    10. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
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    More about this item

    Keywords

    Spatial dynamic panel data model; Short panel data; M-estimation; Fixed effect; Threshold extension;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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