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Is Spatial Bootstrapping a Panacea for Valid Inference?

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Abstract

Bootstrapping methods have so far been rarely used to evaluate spatial data sets. Based on an extensive Monte Carlo study we find that also for spatial, cross-sectional data, the wild bootstrap test proposed by Davidson and Flachaire (2008) based on restricted residuals clearly outperforms asymptotic as well as competing bootstrap tests, like the pairs bootstrap.

Suggested Citation

  • Torben Klarl, 2013. "Is Spatial Bootstrapping a Panacea for Valid Inference?," Discussion Paper Series 322, Universitaet Augsburg, Institute for Economics.
    • Handle: RePEc:aug:augsbe:0322
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    1. Kuan-Pin Lin & Zhi-He Long & Bianling Ou, 2011. "The Size and Power of Bootstrap Tests for Spatial Dependence in a Linear Regression Model," Computational Economics, Springer;Society for Computational Economics, vol. 38(2), pages 153-171, August.
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    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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