The Country‐Product‐Dummy Method With Product‐Specific Spatial Price Variation

ABSTRACT The present paper shows that product‐specific spatial price variation usually causes the Country‐Product‐Dummy (CPD) method to be biased. In cases where it is not, the CPD method is still inefficient and statistical inference is invalid. The paper develops a non‐linear generalization of the CPD method. Even for product‐specific spatial price variation, this NLCPD method remains unbiased and allows for inference. A comprehensive simulation reveals that the NLCPD method's root mean squared error is smaller than that of the Gini‐Eltetö‐Köves‐Szulc (GEKS) index and the CPD method. Finally, this paper applies the NLCPD method to spatial price information derived from Germany's consumer price index micro data of 2019. Price levels of the 401 German districts are computed.

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Publication Details

Journal
Review of Income and Wealth
Published
2025-02-01
DOI
https://doi.org/10.1111/roiw.70005
Primary Topic
Spatial and Panel Data Analysis
Type
article
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article

The Country‐Product‐Dummy Method With Product‐Specific Spatial Price Variation

Sebastian Weinand, Ludwig von Auer
Review of Income and Wealth
Spatial and Panel Data Analysis
article

The Country‐Product‐Dummy Method With Product‐Specific Spatial Price Variation

Sebastian Weinand, Ludwig von Auer
article en

Abstract

ABSTRACT The present paper shows that product‐specific spatial price variation usually causes the Country‐Product‐Dummy (CPD) method to be biased. In cases where it is not, the CPD method is still inefficient and statistical inference is invalid. The paper develops a non‐linear generalization of the CPD method. Even for product‐specific spatial price variation, this NLCPD method remains unbiased and allows for inference. A comprehensive simulation reveals that the NLCPD method's root mean squared error is smaller than that of the Gini‐Eltetö‐Köves‐Szulc (GEKS) index and the CPD method. Finally, this paper applies the NLCPD method to spatial price information derived from Germany's consumer price index micro data of 2019. Price levels of the 401 German districts are computed.

Review of Income and WealthVol. 71(1)
Deutsche Bundesbank (DE), Universität Trier (DE)
Openalex Percentile: Top 95%
Spatial and Panel Data Analysis
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