Panel threshold regressions with latent group structures

DOI10.1016/j.jeconom.2019.07.006zbMath1456.62300OpenAlexW2962824474MaRDI QIDQ2294453

Liangjun Su, Wendun Wang, Ke Miao

Publication date: 11 February 2020

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

Full work available at URL: https://ink.library.smu.edu.sg/soe_research/2285

zbMATH Keywords

classificationdynamic panelpanel structure modellatent group structurespanel threshold regression

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 (6)

Identifying Latent Groups in Spatial Panel Data Using a Markov Random Field Constrained Product Partition Model ⋮ Identifying latent group structures in spatial dynamic panels ⋮ Multi-dimensional latent group structures with heterogeneous distributions ⋮ Estimation of panel group structure models with structural breaks in group memberships and coefficients ⋮ Multi-Threshold Structural Equation Model ⋮ Grouped spatial autoregressive model

Cites Work

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• STRUCTURAL THRESHOLD REGRESSION
• Estimation of Panel Data Models with Parameter Heterogeneity when Group Membership is Unknown
• BIAS REDUCTION FOR DYNAMIC NONLINEAR PANEL MODELS WITH FIXED EFFECTS
• Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices
• Linear Regression Limit Theory for Nonstationary Panel Data
• Sample Splitting and Threshold Estimation
• Determining the number of groups in latent panel structures with an application to income and democracy
• Identifying Latent Structures in Panel Data
• Grouped Patterns of Heterogeneity in Panel Data
• TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS
• Classification of Non-Parametric Regression Functions in Longitudinal Data Models
• Inference When a Nuisance Parameter Is Not Identified Under the Null Hypothesis
• INSTRUMENTAL VARIABLE ESTIMATION OF A THRESHOLD MODEL
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