We develop a new class of tree-based models (P-Tree) for analyzing (unbalanced) panel data utilizing global (instead of local) split criteria that incorporate economic guidance to guard against overfitting while preserving interpretability. We grow a P-Tree top-down to split the cross section of...

Sparse models, though long preferred and pursued by social scientists, can be ineffective or unstable relative to large models, for example, in economic predictions (Giannone et al., 2021). To achieve sparsity for economic interpretation while exploiting big data for superior empirical...

We introduce a class of interpretable tree-based models (P-Tree) for analyzing (unbalanced) panel data, with iterative and global (instead of recursive and local) split criteria. We apply P-Tree to split the cross section of asset returns under the no-arbitrage condition, generating a stochastic...

Asset returns exhibit grouped heterogeneity, and a “one-size-fits-all” model has been elusive empirically. This paper proposes a Bayesian Clustering Model (BCM) combining Bayesian factor selection and panel tree for asset clustering. The Bayesian model marginal likelihood guides the tree...