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Research on income risk typically treats its proxy—income volatility, the expected magnitude of income changes—as if it were unchanged for an individual over time, the same for everyone at a point in time, or both. In reality, income risk evolves over time, and some people face more of it than others. To model heterogeneity and dynamics in (unobserved) income volatility, we develop a novel semiparametric Bayesian stochastic volatility model. Our Markovian hierarchical Dirichlet process (MHDP) prior augments the recently developed hierarchical Dirichlet process (HDP) prior to accommodate the serial dependence of panel data. We document dynamics and substantial heterogeneity in income volatility.
Hierarchical Dirichlet process; income volatility; state-space models
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All findings, interpretations, and conclusions of this paper also do not represent the views of the Wharton School or the Boettner Center for Pensions and Retirement Research. © 2012 Boettner Center of the Wharton School of the University of Pennsylvania. All rights reserved.
The project described was supported by the National Institute on Aging, P30 AG-012836-18, the Boettner Center for Pensions and Retirement Security, and National Institutes of Health – National Institute of Child Health and Development Population Research Infrastructure Program R24 HD-044964-9, all at the University of Pennsylvania. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Aging or the National Institutes of Health.
Date Posted: 28 June 2019