Virtual Room 3: Panel Data


Thursday, July 16, 2020, 2:30pm to 4:00pm


Chair: Suzanna Linn (Penn State University)


Co-Host: Ilayda Onder (Penn State University)

Bayesian Causal Inference With Time-Series Cross-Sectional Data: A Dynamic Multilevel Latent Factor Model with Hierarchical Shrinkage

Author(s): Yiqing Xu, Xun Pang and Licheng Liu

Discussant: Neal Beck (New York University)


This paper proposes a Bayesian causal inference method based on estimating posterior predictive distributions of counterfactuals with TSCS data. To construct the prediction model, we fully take advantage of the flexibility of multilevel modeling and Bayesian model specification to reduce dependence on modeling assumptions. We start with a multilevel dynamic factor model and adopt a Bayesian Lasso-like hierarchical shrinkage strategy for stochastic model-specification selection. Counterfactual imputation based on the posterior predictive distribution generalizes the classic synthetic control approach by assigning observation-specific weights to features of the treated units and exploiting high-order relationships between treated and control time series. With empirical posterior distributions of counterfactuals, it is convenient and intuitive to make causal inferences on estimands defined at the individual and aggregate levels. The proposed approach is applied to simulated data and two empirical examples as in ADH (2015) and Xu (2017). The applications illustrate that, compared to alternative approaches, our method has better counterfactual prediction performance and lower uncertainty and accordingly improves causal inference with TSCS data.

A Nonparametric Bayesian Model for Gradual Structural Changes: The Intergenerational Chinese Restaurant Processes

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Author(s): Nuannuan Xiang and Yuki Shiraito

Discussant: Mark Pickup (Simon Fraser University)


Many social changes occur gradually over time, and social scientists are often interested in such changes of unobserved heterogeneity. However, existing methods for estimating structural changes have failed to model continuous processes through which a data generating process evolves. This paper proposes a novel nonparametric Bayesian model to flexibly estimate changing heterogeneous data generating processes. By introducing a time dynamic to the Dirichlet process mixture model, the proposed intergenerational Chinese restaurant process (IgCRP) model categorizes units into groups and allows the group memberships to evolve as a Markov process. In the IgCRP, the group assigned to a unit in a time period follows the standard Chinese restaurant process conditional on the group assignments in the previous time period. A distinctive feature of the proposed approach is that it models a process in which multiple groups emerge and diminish as a continuing process rather than a one-time structural change. The method is illustrated by reanalyzing the data set of a study on the evolution of party positions on civil rights in the United States from the 1930s to the 1960s.

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