Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity

Ando, Tomohiro and Bai, Jushan (2021): Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity.

Abstract

This paper provides methods for flexibly capturing unobservable heterogeneity from longitudinal data in the context of an exponential family of distributions. The group memberships of individual units are left unspecified, and their heterogeneity is influenced by group-specific unobservable structures, as well as heterogeneous regression coefficients. We discuss a computationally efficient estimation method and derive the corresponding asymptotic theory. The established asymptotic theory includes verifying the uniform consistency of the estimated group membership. To test the heterogeneous regression coefficients within groups, we propose the Swamy-type test, which considers unobserved heterogeneity. We apply the proposed method to study the market structure of the taxi industry in New York City. Our method reveals interesting important insights from large-scale longitudinal data that consist of over 450 million data points.

• Item Type: MPRA Paper
• Language: English
• Keywords: Clustering; Factor analysis; Generalized linear models; Longitudinal data; Unobserved heterogeneity.
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis

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