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The SHAC estimator in panel data with group-specific spatial lags

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  • James Schmidt

  • Hoa Tran

Abstract

An adaptation of the spatial heteroskedasticity autocorrelation consistent (SHAC) estimator from the context of a single cross section to a panel data environment is presented. The spatial model involved with the SHAC estimator is generalized to allow spatial lag parameters and spatial weights to vary across groups of cross section members. A model of earnings growth in the 48 contiguous states of the U.S. grouped into eight subnational regions, is used as an illustration. The traditional production inputs of labor, physical capital, and human capital are significant determinants of earnings growth. Significant differences between the spatial lag parameters of subnational regions are found. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • James Schmidt & Hoa Tran, 2014. "The SHAC estimator in panel data with group-specific spatial lags," Letters in Spatial and Resource Sciences, Springer, vol. 7(2), pages 61-71, July.
    • Handle: RePEc:spr:lsprsc:v:7:y:2014:i:2:p:61-71
    DOI: 10.1007/s12076-013-0101-z
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    References listed on IDEAS

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    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
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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