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Inference Based on Alternative Bootstrapping Methods in Spatial Models with an Application to County Income Growth in the United States

Author

Listed:
  • Monchuk, Daniel C.
  • Hayes, Dermot J.
  • Miranowski, John
  • Lambert, Dayton

Abstract

This study examines aggregate county income growth across the 48 contiguous states from 1990 to 2005. To control for endogeneity, we estimate a two-stage spatial error model and implement a number of spatial bootstrap routines to infer parameter significance. Among the results, we find that outdoor recreation and natural amenities favor positive growth in rural counties and property taxes correlate negatively with rural growth. Comparing bootstrap inference with other models, including the recent General Moment heteroskedastic-robust spatial error estimator, we find similar conclusions suggesting bootstrapping can be effective in spatial models where asymptotic results are not well established.

Suggested Citation

  • Monchuk, Daniel C. & Hayes, Dermot J. & Miranowski, John & Lambert, Dayton, 2013. "Inference Based on Alternative Bootstrapping Methods in Spatial Models with an Application to County Income Growth in the United States," Staff General Research Papers Archive 36121, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:36121
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    Citations

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    Cited by:

    1. Andrés Rodríguez-Pose & Tobias D. Ketterer, 2012. "Do Local Amenities Affect The Appeal Of Regions In Europe For Migrants?," Journal of Regional Science, Wiley Blackwell, vol. 52(4), pages 535-561, October.
    2. Dayton M. Lambert, 2020. "Dynamic panel estimation of a regional adjustment model with spatial-temporal robust covariance," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 245-265, December.
    3. Frank Davenport, 2017. "Estimating standard errors in spatial panel models with time varying spatial correlation," Papers in Regional Science, Wiley Blackwell, vol. 96, pages 155-177, March.
    4. Marcos Herrera & Manuel Ruiz & Jesús Mur, 2013. "Detecting Dependence Between Spatial Processes," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(4), pages 469-497, February.
    5. Mark D. Partridge & Marlon Boarnet & Steven Brakman & Gianmarco Ottaviano, 2012. "Introduction: Whither Spatial Econometrics?," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 167-171, May.
    6. Jin, Fei & Lee, Lung-fei, 2015. "On the bootstrap for Moran’s I test for spatial dependence," Journal of Econometrics, Elsevier, vol. 184(2), pages 295-314.
    7. Torben Klarl, 2014. "Is Spatial Bootstrapping A Panacea For Valid Inference?," Journal of Regional Science, Wiley Blackwell, vol. 54(2), pages 304-312, March.
    8. Paredes, Dusan & Loveridge, Scott, 2018. "Rural electric cooperatives and economic development," Energy Policy, Elsevier, vol. 117(C), pages 49-57.
    9. Ou Bianling & Long Zhihe & Li Wenqian, 2019. "Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects," Journal of Systems Science and Information, De Gruyter, vol. 7(4), pages 330-343, August.
    10. Dayton M. Lambert & Wan Xu & Raymond J. G. M. Florax, 2014. "Partial Adjustment Analysis of Income and Jobs, and Growth Regimes in the Appalachian Region with Smooth Transition Spatial Process Models," International Regional Science Review, , vol. 37(3), pages 328-364, July.

    More about this item

    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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