Identifying latent grouped patterns in panel data models with interactive fixed effects

• Main Authors: SU, Liangjun, JU, Gaosheng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Classifier Lasso; Cross section dependence; Dynamic panel; High dimensionality; Latent structure; Parameter heterogeneity; Penalized method; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2192; https://ink.library.smu.edu.sg/context/soe_research/article/3191/viewcontent/Panel_structure_interactive_fixed_effects_20160808_pp.pdf
• Institution: Singapore Management University

We consider the estimation of latent grouped patterns in dynamic panel data models with interactive fixed effects. We assume that the individual slope coefficients are homogeneous within a group and heterogeneous across groups but each individual’s group membership is unknown to the researcher. We consider penalized principal component (PPC) estimation by extending the penalized-profile-likelihood-based C-Lasso of Su, Shi, and Phillips (2016) to panel data models with cross section dependence. Given the correct number of groups, we show that the C-Lasso can achieve simultaneous classification and estimation in a single step and exhibit the desirable property of uniform classification consistency. The C-Lasso-based PPC estimators of the group-specific parameters also have the oracle property. BIC-type information criteria are proposed to choose the numbers of factors and groups consistently and to select the data-driven tuning parameter. Simulations are conducted to demonstrate the finite-sample performance of the proposed method. We apply our C-Lasso to study the persistence of housing prices in China’s large and medium-sized cities in the last decade and identify three groups.