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

Giovanni Millo ()

Regional Science and Urban Economics, 2017, vol. 66, issue C, 28-38

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.

Keywords: Panel data; Common factors; Spatial dependence; Serial correlation; Randomization test (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1016/j.regsciurbeco.2017.05.004

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