Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-05T12:12:28.780Z Has data issue: false hasContentIssue false

A unified stability theory for classical and monotone Markov chains

Published online by Cambridge University Press:  12 July 2019

Takashi Kamihigashi*
Affiliation:
Kobe University
John Stachurski*
Affiliation:
Australian National University
*
*Postal address: Research Institute of Economics and Business, Kobe University, Japan. Email address: [email protected]
**Postal address: Research School of Economics, Australian National University, Australia. Email address: [email protected]

Abstract

In this paper we integrate two strands of the literature on stability of general state Markov chains: conventional, total-variation-based results and more recent order-theoretic results. First we introduce a complete metric over Borel probability measures based on ‘partial’ stochastic dominance. We then show that many conventional results framed in the setting of total variation distance have natural generalizations to the partially ordered setting when this metric is adopted.

Type
Research Papers
Copyright
© Applied Probability Trust 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bhattacharya, R. and Lee, O. (1988). Asymptotics of a class of Markov processes which are not in general irreducible. Ann. Prob. 16, 13331347.CrossRefGoogle Scholar
Bhattacharya, R., Majumdar, M. and Hashimzade, N. (2010). Limit theorems for monotone Markov processes. Sankhyā A 72, 170190.CrossRefGoogle Scholar
Chakraborty, S. and Rao, B. (1998). Completeness of the Bhattacharya metric on the space of probabilities. Statist. Probab. Lett. 36, 321326.CrossRefGoogle Scholar
Diaconis, P. and Freedman, D. (1999). Iterated random functions. SIAM Review 41, 4576.Google Scholar
Dobrushin, R. L. (1956). Central limit theorem for nonstationary Markov chains. Theory Prob. Appl. 1, 6580.CrossRefGoogle Scholar
Doeblin, W. (1937). Sur les propriétés asymptotiques de mouvements régis par certains types de chaînes simples. Bull. Math. Soc. Roum. Sci. 39, (1) 57115; (2), 3–61.Google Scholar
Doeblin, W. (1938). Exposé de la théorie des chaînes simples constantes de Markov à un nombre fini d’états. Mathématique de l’Union Interbalkanique 2, 7880.Google Scholar
Doeblin, W. (1940). Éléments d’une théorie générale des chaînes simples constantes de Markoff. In Ann. Sci. Ec. Norm. Supér. 57, 61111.CrossRefGoogle Scholar
Doob, J. L. (1953). Stochastic Processes. Wiley, New York.Google Scholar
Dubins, L. E. and Freedman, D. A. (1966). Invariant probabilities for certain Markov processes. Ann. Math. Statist. 37, 837848.CrossRefGoogle Scholar
Dudley, R. (2002). Real Analysis and Probability. Cambridge University Press.CrossRefGoogle Scholar
Gibbs, A. L. and Su, F. E. (2002). On choosing and bounding probability metrics. Int. Stat. Rev. 70, 419435.CrossRefGoogle Scholar
Givens, C. R., Shortt, R. M. (1984). A class of Wasserstein metrics for probability distributions. Michigan Math. J. 31, 231240.Google Scholar
Harris, T. E. (1956). The existence of stationary measures for certain Markov processes. In Proc. Third Berkeley Symp. on Mathematical Statistics and Probability, Vol. 2, University of California Press, pp. 113124.Google Scholar
Hernández-Lerma, O. and Lasserre, J. (2003). Markov Chains and Invariant Probabilities. Springer.CrossRefGoogle Scholar
Hopenhayn, H. A. and Prescott, E. C. (1992). Stochastic monotonicity and stationary distributions for dynamic economies. Econometrica 60, 13871406.Google Scholar
Jarner, S. and Tweedie, R. (2001). Locally contracting iterated functions and stability of Markov chains. J. Appl. Prob. 38, 494507.CrossRefGoogle Scholar
Kamae, T. and Krengel, U. (1978). Stochastic partial ordering. Ann. Prob. 6, 10441049.CrossRefGoogle Scholar
Kamae, T., Krengel, U. and O’Brien, G. (1977). Stochastic inequalities on partially ordered spaces. Ann. Prob. 5, 899912.CrossRefGoogle Scholar
Kamihigashi, T. and Stachurski, J. (2012). An order-theoretic mixing condition for monotone Markov chains. Statist. Probab. Lett. 82, 262267.CrossRefGoogle Scholar
Kamihigashi, T. and Stachurski, J. (2014). Stochastic stability in monotone economies. Theoret. Econom. 9, 383407.Google Scholar
Lindvall, T. (2002). Lectures on the Coupling Method. Dover.Google Scholar
Machida, M. and Shibakov, A. (2010). Monotone bivariate Markov kernels with specified marginals. Proc. Amer. Math. Soc. 138, 21872194.Google Scholar
Markov, A. A. (1906). Extension of the law of large numbers to dependent quantities. Izv. Fiz.-Matem. Obsch. Kazan Univ. (2nd ser.) 15, 135156.Google Scholar
Meyn, S. P. and Tweedie, R. L. (2012). Markov Chains and Stochastic Stability. Springer Science & Business Media.Google Scholar
Nummelin, E. (2004). General Irreducible Markov Chains and Non-Negative Operators. Cambridge University Press.Google Scholar
Orey, S. (1959). Recurrent Markov chains. Pacific J. Math. 9, 805827.CrossRefGoogle Scholar
Pitman, J. W. (1974). Uniform rates of convergence for Markov chain transition probabilities. Prob. Theory Relat. Fields 29, 193227.Google Scholar
Pollard, D. (2002). A User’s Guide to Measure Theoretic Probability. Cambridge University Press.Google Scholar
Razin, A. and Yahav, J. A. (1979). On stochastic models of economic growth. Internat. Econom. Review 20, 599604.CrossRefGoogle Scholar
Revuz, D. (2008). Markov Chains. Elsevier.Google Scholar
Seneta, E. (1979). Coefficients of ergodicity: structure and applications. Adv. Appl. Prob. 11, 576590.CrossRefGoogle Scholar
Strassen, V. (1965). The existence of probability measures with given marginals. Ann. Math. Statist. 36, 423439.Google Scholar
Thorisson, H. (2000). Coupling, Stationarity, and Regeneration. Springer, New York.CrossRefGoogle Scholar
Wu, W. B. and Shao, X. (2004). Limit theorems for iterated random functions. J. Appl. Prob. 41, 425436.CrossRefGoogle Scholar
Yahav, J. A. (1975). On a fixed point theorem and its stochastic equivalent. J. Appl. Prob. 12, 605611.Google Scholar
Yosida, K. and Kakutani, S. (1941). Operator-theoretical treatment of Markoff’s process and mean ergodic theorem. Ann. Math. 42, 188228.Google Scholar