慶応義塾大学 経済研究所

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Testing for localization: A new approach

村田安寧、中嶋亮、田村龍一

2017年5月3日

JELコード : R12, O31

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【要旨】

Recent empirical studies document that knowledge spillovers attenuate and industry localization decays with distance. It is thus imperative to detect localization accurately especially at short distances. We propose a new approach to testing for localization that corrects the first-order bias at and near the boundary in existing methods while retaining all desirable properties at interior points. Employing the NBER U.S. Patent Citations Data File, we illustrate the performance of our localization measure based on local linear density estimators. Our results suggest that the existing kernel density methods and regression approaches can be substantially biased at short distances.