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Dynamic Spatial Panels: Models, Methods and Inferences

In: Spatial Econometrics

Author

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  • J. Paul Elhorst

    (University of Groningen)

Abstract
This chapter provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent variable lagged in space, a dependent variable lagged in both space and time, independent variables lagged in time, independent variables lagged in space, serial error autocorrelation, spatial error autocorrelation, spatial-specific and time-period specific effects. The well-known Baltagi and Li (2004) panel dataset, explaining cigarette demand for 46 US states over the period 1963 to 1992, is used to investigate whether the extension of a non-dynamic to a dynamic spatial panel data specification increases the explanatory power of the model.

Suggested Citation

  • J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.
    • Handle: RePEc:spr:sbrchp:978-3-642-40340-8_4
    DOI: 10.1007/978-3-642-40340-8_4
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    More about this item

    Keywords

    Dynamic Effects; Estimation methods; Stationarity conditions; Endogeneity; Non-stability; Spatial spillover effects; Cigarette demand;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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