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Spatial Panel Models and Common Factors

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Abstract

This chapter provides a survey of the existing literature on spatial panel data models. Both static, dynamic, and dynamic models with common factors will be considered. Common factors are modeled by time-period fixed effects, cross-sectional averages, or principal components. It is demonstrated that spatial econometric models that include lags of the dependent variable and of the independent variables in both space and time provide a useful tool to quantify the magnitude of direct and indirect effects, both in the short term and long term. Direct effects can be used to test the hypothesis as to whether a particular variable has a significant effect on the own dependent variable, and indirect effects to test the hypothesis whether spatial spillovers affect the dependent variable of other units. To illustrate these models, their effects estimates, and the impact of the type of common factors, a demand model for cigarettes is estimated based on panel data from 46 U.S. states over the period 1963 to 1992.

Keywords

  • Spatial panels
  • Dynamic effects
  • Spatial spillovers
  • Common factors
  • Estimation

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  • DOI: 10.1007/978-3-662-60723-7_86
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References

  • Allers MA, Elhorst JP (2005) Tax mimicking and yardstick competition among governments in the Netherlands. Int Tax Public Financ 12(4):493–513

    CrossRef  Google Scholar 

  • Anselin L (1988) Spatial econometrics: methods and models. Kluwer, Dordrecht

    CrossRef  Google Scholar 

  • Anselin L, Le Gallo J, Jayet H (2008) Spatial panel econometrics. In: Mátyás L, Sevestre P (eds) The econometrics of panel data, fundamentals and recent developments in theory and practice, 3rd edn. Kluwer, Dordrecht, pp 627–662

    Google Scholar 

  • Baltagi BH (2005) Econometric analysis of panel data, 3rd edn. Wiley, Chichester

    Google Scholar 

  • Baltagi BH, Levin D (1992) Cigarette taxation: raising revenues and reducing consumption. Struct Chang Econ Dyn 3(2):321–335

    CrossRef  Google Scholar 

  • Belotti F, Hughes G, Mortari AP (2017) Spatial panel-data models using Stata. Stata J 17(1):139–180

    CrossRef  Google Scholar 

  • Burridge P, Elhorst JP, Zigova K (2017) Group interaction in research and the use of general nesting spatial models. In: Baltagi BH, LeSage JP, Pace RK (eds) Spatial econometrics: qualitative and limited dependent variables, Advances in econometrics, vol 37. Emerald, Bingley, pp 223–258

    CrossRef  Google Scholar 

  • Ciccarelli C, Elhorst JP (2018) A dynamic spatial econometric diffusion model with common factors: the rise and spread of cigarette consumption in Italy. Reg Sci Urban Econ 72:131–142

    CrossRef  Google Scholar 

  • Debarsy N, Ertur C, LeSage JP (2012) Interpreting dynamic space-time panel data models. Stat Methodol 9(1–2):158–171

    CrossRef  Google Scholar 

  • Elhorst JP (2010) Dynamic panels with endogenous interaction effects when T is small. Reg Sci Urban Econ 40(5):272–282

    CrossRef  Google Scholar 

  • Elhorst JP (2014) Spatial econometrics: from cross-sectional data to spatial panels. Springer, Heidelberg/New York/Dordrecht/London

    CrossRef  Google Scholar 

  • Elhorst JP, Madre J-L, Pirotte A (2018) Car traffic, habit persistence, cross-sectional dependence, and spatial heterogeneity: new Insights using French departmental data. University of Groningen, working paper under review

    Google Scholar 

  • Ertur C, Koch W (2007) Growth, technological interdependence and spatial externalities: theory and evidence. J Appl Econ 22(6):1033–1062

    CrossRef  Google Scholar 

  • Halleck Vega S, Elhorst JP (2015) The SLX model. J Reg Sci 55(3):339–363

    CrossRef  Google Scholar 

  • Kelejian HH, Piras G (2014) Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes. Reg Sci Urban Econ 46:140–149

    CrossRef  Google Scholar 

  • Kelejian HH, Piras G (2017) Spatial econometrics. Elsevier, London

    Google Scholar 

  • Lee LF, Yu J (2015) Spatial panel data models. In: Baltagi BH (ed) The Oxford handbook of panel data. University Press, Oxford, pp 363–401

    Google Scholar 

  • Lee LF, Yu J (2016) Identification of spatial Durbin models. J Appl Econ 31(1):133–162

    CrossRef  Google Scholar 

  • LeSage JP, Pace RK (2009) Introduction to spatial econometrics. CRC Press/Taylor & Francis Group, Boca Raton

    CrossRef  Google Scholar 

  • Parent O, LeSage JP (2010) A spatial dynamic panel model with random effects applied to commuting times. Transp Res B 44(5):633–645

    CrossRef  Google Scholar 

  • Pesaran MH (2006) Estimation and inferences in large heterogenous panels with a multifactor structure. Econometrica 74(4):967–1012

    CrossRef  Google Scholar 

  • Pesaran MH (2015a) Time series and panel data econometrics. Oxford University Press, Oxford

    CrossRef  Google Scholar 

  • Pesaran MH (2015b) Testing weak cross-sectional dependence in large panels. Econ Rev 34(6–10):1089–1116

    CrossRef  Google Scholar 

  • Shi W, Lee LF (2017) Spatial dynamic panel data model with interactive fixed effects. J Econ 197(2):323–347

    CrossRef  Google Scholar 

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Correspondence to J. Paul Elhorst .

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Elhorst, J.P. (2021). Spatial Panel Models and Common Factors. In: Fischer, M.M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-60723-7_86

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