Simultaneous clustering of individuals and covariates for high‐dimensional longitudinal data

Summary This paper considers identifying and estimating high‐dimensional longitudinal data models with latent subgroups and clustered covariates. We propose a regularised approach to recover group structures while simultaneously detecting clusters of significant covariates. The consistency and asymptotic normality are established for the estimator under mild conditions. Besides, we develop an effective algorithm based on local quadratic approximation to optimise the objective function. The finite‐sample performance is illustrated through extensive simulations, and the proposed method is applied to study the shift in the economic structure of European countries before and after the debt crisis.