Identifying latent group structures in nonlinear panels

DOI10.1016/j.jeconom.2020.04.003zbMath1464.62522OpenAlexW2779756914MaRDI QIDQ2224976

Liangjun Su, Wuyi Wang

Publication date: 4 February 2021

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://ink.library.smu.edu.sg/soe_research/2120

zbMATH Keywords

networkclusteringsingular value decompositioncommunity detectionbinary segmentation algorithmoracle estimatorparameter heterogeneitypanel structure model

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (16)

Estimation and identification of latent group structures in panel data ⋮ Group structure detection for a high‐dimensional panel data model ⋮ Community network auto-regression for high-dimensional time series ⋮ Identifying latent group structures in spatial dynamic panels ⋮ Unnamed Item ⋮ Detecting Unobserved Heterogeneity in Efficient Prices via Classifier-Lasso ⋮ Modeling panels of extremes ⋮ IDENTIFYING LATENT GROUPED PATTERNS IN COINTEGRATED PANELS ⋮ Multi-dimensional latent group structures with heterogeneous distributions ⋮ Heterogeneous analysis for clustered data using grouped finite mixture models ⋮ Large-Scale Generalized Linear Models for Longitudinal Data with Grouped Patterns of Unobserved Heterogeneity ⋮ Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models ⋮ Homogeneity and Sparsity Analysis for High-Dimensional Panel Data Models ⋮ Panel data models with time-varying latent group structures ⋮ Unnamed Item ⋮ A blockwise network autoregressive model with application for fraud detection

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