spatial panels

Matlab Software for Spatial Panels. Elhorst provides Matlab routines to estimate spatial panel data models at his website. This article extends these routines to include the bias correction procedure proposed by Lee and Yu if the spatial panel data model contains spatial and/or time-period fixed effects, the direct and indirect effects estimates of the explanatory variables proposed by LeSage and Pace, and a selection framework to determine which spatial panel data model best describes the data. To demonstrate these routines in an empirical setting, a demand model for cigarettes is estimated based on panel data from forty-six US states over the period 1963–1992.


References in zbMATH (referenced in 20 articles )

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  1. Baltagi, Badi H.; Pirotte, Alain; Yang, Zhenlin: Diagnostic tests for homoskedasticity in spatial cross-sectional or panel models (2021)
  2. Desbordes, Rodolphe: Spatial dynamics of major infectious diseases outbreaks: a global empirical assessment (2021)
  3. Skevas, Ioannis; Skevas, Theodoros: A generalized true random-effects model with spatially autocorrelated persistent and transient inefficiency (2021)
  4. Yang, Cynthia Fan: Common factors and spatial dependence: an application to US house prices (2021)
  5. Yildirim, Julide; Alpaslan, Barış; Eker, Erdener Emin: The role of social capital in environmental protection efforts: evidence from Turkey (2021)
  6. Zhang, Cheng; Qian, Li-Xian; Hu, Jian-Qiang: COVID-19 pandemic with human mobility across countries (2021)
  7. Chen, Xia; Liu, Jianmin: Fiscal decentralization and environmental pollution: a spatial analysis (2020)
  8. Klein, Nadja; Herwartz, Helmut; Kneib, Thomas: Modelling regional patterns of inefficiency: a Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales (2020)
  9. Mínguez, Román; Basile, Roberto; Durbán, María: An alternative semiparametric model for spatial panel data (2020)
  10. Bera, Anil K.; Doğan, Osman; Taşpınar, Süleyman: Testing spatial dependence in spatial models with endogenous weights matrices (2019)
  11. Ramírez Hassan, Andrés; Montoya Blandón, Santiago: Welfare gains of the poor: an endogenous Bayesian approach with spatial random effects (2019)
  12. Yu, T. Edward; Sharma, Bijay P.; English, Burton C.: Investigating lock delay on the upper mississippi river: a spatial panel analysis (2019)
  13. Song, Malin; Peng, Jun; Wang, Jianlin; Zhao, Jiajia: Environmental efficiency and economic growth of China: a ray slack-based model analysis (2018)
  14. Liu, Jianmin; Chen, Xia; Wei, Runchu: Socioeconomic drivers of environmental pollution in China: a spatial econometric analysis (2017)
  15. Glass, Anthony J.; Kenjegalieva, Karligash; Sickles, Robin C.: A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers (2016)
  16. LeSage, James P.; Chih, Yao-Yu: Interpreting heterogeneous coefficient spatial autoregressive panel models (2016)
  17. Mitze, Timo: On the mutual dynamics of interregional gross migration flows in space and time (2016)
  18. Ohtsuka, Yoshihiro: Estimation of marginal effects and their spillover effects in productivity of prefectures (2015)
  19. Wang, Wei; Yu, Jihai: Estimation of spatial panel data models with time varying spatial weights matrices (2015)
  20. Elhorst, J. Paul: Spatial econometrics. From cross-sectional data to spatial panels (2014)