Yuki Shiraito, University of Michigan

Abstract: 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.