Quantitative Convergence Rates for Stochastically Monotone Markov Chains

Takashi Kamihigashi and John Stachurski
Additional contact information
John Stachurski: Research School of Economics, Australian National University, AUSTRALIA

No DP2024-32, Discussion Paper Series from Research Institute for Economics & Business Administration, Kobe University

Abstract: For Markov chains and Markov processes exhibiting a form of stochastic monotonicity (larger states shift up transition probabilities in terms of stochastic dominance), stability and ergodicity results can be obtained using order-theoretic mixing conditions. We complement these results by providing quantitative bounds on deviations between distributions. We also show that well-known total variation bounds can be recovered as a special case.

Pages: 12 pages
Date: 2024-10
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rieb.kobe-u.ac.jp/academic/ra/dp/English/DP2024-32.pdf First version, 2024 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kob:dpaper:dp2024-32

Access Statistics for this paper

More papers in Discussion Paper Series from Research Institute for Economics & Business Administration, Kobe University 2-1 Rokkodai, Nada, Kobe 657-8501 JAPAN. Contact information at EDIRC.
Bibliographic data for series maintained by Office of Promoting Research Collaboration, Research Institute for Economics & Business Administration, Kobe University (kenjo@rieb.kobe-u.ac.jp).