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A simple randomization test for spatial correlation in the presence of common factors and serial correlation

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  • Millo, Giovanni

Abstract

A randomization test is proposed for detecting spatial dependence in panel models with cross-sectional dependence induced by an unobserved common factor structure. Spatial dependence is related to the position of observations in space while cross-sectional dependence is generally not; yet spatial correlation tests have power against both. Permuting the pairs of neighbouring observations in the proximity matrix yields a simple spatial dependence test which is robust to the presence of non-spatial cross-sectional correlation, serial correlation and can accommodate short and unbalanced panels. The proposed procedure is evaluated and compared to alternatives through Monte Carlo simulation; it is then illustrated by an application to recent research on technology spillovers. A user-friendly R implementation is provided.

Suggested Citation

  • Millo, Giovanni, 2017. "A simple randomization test for spatial correlation in the presence of common factors and serial correlation," Regional Science and Urban Economics, Elsevier, vol. 66(C), pages 28-38.
    • Handle: RePEc:eee:regeco:v:66:y:2017:i:c:p:28-38
    DOI: 10.1016/j.regsciurbeco.2017.05.004
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    References listed on IDEAS

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    7. Carmelo Algeri & Luc Anselin & Antonio Fabio Forgione & Carlo Migliardo, 2022. "Spatial dependence in the technical efficiency of local banks," Papers in Regional Science, Wiley Blackwell, vol. 101(3), pages 685-716, June.
    8. Alexey, Kurbatskiy & Nikita, Artamonov & Timur, Khalimov, 2020. "Взаимосвязь Экономического Развития И Возрастной Структуры Населения Регионов Российской Федерации [Relationship between economic development and the population age structure of Russian Federation ," MPRA Paper 105273, University Library of Munich, Germany.

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