Semiparametric Bayesian Modeling of Income Volatility Heterogeneity

Autor:	Stephen H. Shore, Shane T. Jensen
Rok vydání:	2011
Předmět:	Statistics and Probability Hierarchical Dirichlet process Stochastic volatility Bayesian probability Markov process Implied volatility Bayesian inference symbols.namesake Statistics Economics symbols Econometrics Statistics Probability and Uncertainty Volatility (finance) Panel data
Zdroj:	Journal of the American Statistical Association. 106:1280-1290
ISSN:	1537-274X 0162-1459
DOI:	10.1198/jasa.2011.ap09283
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d05d6cc7fadb0fd5a8304ca19e168f20 https://doi.org/10.1198/jasa.2011.ap09283 Zobrazit plný text záznamu