Identifying latent structures in panel data

• Main Authors: SU, Liangjun, SHI, Zhentao, Peter C. B. PHILLIPS
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Classification; cluster analysis; dynamic panel; group Lasso; high dimensionality; nonlinear panel; oracle property; panel structure model; parameter heterogeneity; penalized least squares; penalized GMM; penalized profile likelihood; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/1911; https://ink.library.smu.edu.sg/context/soe_research/article/2910/viewcontent/IdentifyingLatentStructuresPanelData_2014Jul_pp.pdf
• Institution: Singapore Management University

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membershi...