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Impact Estimates for Static Spatial Panel Data Models in R

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  • Gianfranco Piras

    (Regional Research Institute, West Virginia University)

Abstract

In the present note we demonstrate how to implement the Lee and Yu (2010) procedure for fixed effects spatial panel data models available from the R (R Development Core Team 2012) package splm (Millo and Piras 2012). Additionally, we also show how to compute the impact estimates introduced by Kelejian, Tavlas, and Hondroyiannis (2006) and formalized in LeSage and Pace (2009). Unlike Matlab (MATLAB 2011), there was no R function specific to static panel data models for the calculation of the impact measures. After receiving numerous requests from the users of splm, we decided to extend the cross sectional functions available from spdep (Bivand 2013) to spatial panel data models.

Suggested Citation

  • Gianfranco Piras, 2013. "Impact Estimates for Static Spatial Panel Data Models in R," Working Papers Working Paper 2013-05, Regional Research Institute, West Virginia University.
    • Handle: RePEc:rri:wpaper:2013wp05
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    References listed on IDEAS

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    1. repec:rri:wpaper:201307 is not listed on IDEAS
    2. Debarsy, Nicolas & Ertur, Cem, 2010. "Testing for spatial autocorrelation in a fixed effects panel data model," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 453-470, November.
    3. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    4. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    5. Alicia H. Munnell, 1990. "Why has productivity growth declined? Productivity and public investment," New England Economic Review, Federal Reserve Bank of Boston, issue Jan, pages 3-22.
    6. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
    7. Millo, Giovanni & Piras, Gianfranco, 2012. "splm: Spatial Panel Data Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i01).
    8. Piras, Gianfranco & Prucha, Ingmar R., 2014. "On the finite sample properties of pre-test estimators of spatial models," Regional Science and Urban Economics, Elsevier, vol. 46(C), pages 103-115.
    9. Harry Kelejian & George Tavlas & George Hondroyiannis, 2006. "A Spatial Modelling Approach to Contagion Among Emerging Economies," Open Economies Review, Springer, vol. 17(4), pages 423-441, December.
    10. Roger Bivand & Gianfranco Piras, 2012. "Comparing estimation methods for spatial econometrics," ERSA conference papers ersa12p366, European Regional Science Association.
    11. repec:rri:wpaper:201301 is not listed on IDEAS
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    Cited by:

    1. Reinhold Kosfeld & Timo Mitze & Johannes Rode & Klaus Wälde, 2021. "The Covid‐19 containment effects of public health measures: A spatial difference‐in‐differences approach," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 799-825, September.
    2. Konan Alain N'Ghauran & Corinne Autant-Bernard, 2020. "Effects of cluster policies on regional innovation networks: Evidence from France," Working Papers halshs-02482565, HAL.
    3. Reinhold Kosfeld & Christian Dreger, 2018. "Local and spatial cointegration in the wage curve – a spatial panel analysis for german regions," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 38(1), pages 53-75, February.
    4. Altonji, Matthew & Lang, Corey & Puggioni, Gavino, 2016. "Can urban areas help sustain the preservation of open space? Evidence from statewide referenda," Ecological Economics, Elsevier, vol. 130(C), pages 82-91.
    5. Felipe J. Fonseca & Irving Llamosas-Rosas, 2019. "Spatial linkages and third-region effects: evidence from manufacturing FDI in Mexico," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(2), pages 265-284, April.
    6. Konan Alain N’Ghauran & Corinne Autant-Bernard, 2020. "Effects of cluster policies on regional innovation networks: Evidence from France," Working Papers 2005, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    7. Llamosas-Rosas Irving & Fonseca Felipe J., 2018. "Determinants of FDI Attraction in the Manufacturing Sector in Mexico, 1999-2015," Working Papers 2018-07, Banco de México.

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    More about this item

    Keywords

    spatial panel data models; R; computational methods; impact measures;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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