Grouped Patterns of Heterogeneity in Panel Data

• Stéphane Bonhomme ^[1] ; Elena Manresa ^[1]
  1. [1] CEMFI
• Localización: Documentos de Trabajo ( CEMFI ), Nº. 8 (CEMFI Working Paper 1208, June 2012), 2012
• Idioma: inglés
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• Resumen

  • This paper introduces time-varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unspecified. We estimate the model’s parameters using a “grouped fixed-effects” estimator that minimizes a least-squares criterion with respect to all possible groupings of the cross-sectional units. We rely on recent advances in the clustering literature for fast and efficient computation. Our estimator is higher-order unbiased as both dimensions of the panel tend to infinity, under conditions that we characterize. As a result, inference is not affected by the fact that group membership is estimated. We apply our approach to study the link between income and democracy across countries, while allowing for grouped patterns of unobserved heterogeneity. The results shed new light on the evolution of political and economic outcomes of countries.