A tractable framework for analyzing a class of nonstationary Markov models

Lilia Maliar, Serguei Maliar, John Taylor and Inna Tsener

Quantitative Economics, 2020, vol. 11, issue 4, 1289-1323

Abstract: We consider a class of infinite‐horizon dynamic Markov economic models in which the parameters of utility function, production function, and transition equations change over time. In such models, the optimal value and decision functions are time‐inhomogeneous: they depend not only on state but also on time. We propose a quantitative framework, called extended function path (EFP), for calibrating, solving, simulating, and estimating such nonstationary Markov models. The EFP framework relies on the turnpike theorem which implies that the finite‐horizon solutions asymptotically converge to the infinite‐horizon solutions if the time horizon is sufficiently large. The EFP applications include unbalanced stochastic growth models, the entry into and exit from a monetary union, information news, anticipated policy regime switches, deterministic seasonals, among others. Examples of MATLAB code are provided.

Date: 2020
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Citations: View citations in EconPapers (4)

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https://doi.org/10.3982/QE1360

Related works:
Working Paper: A Tractable Framework for Analyzing a Class of Nonstationary Markov Models (2015)
Working Paper: A Tractable Framework for Analyzing a Class of Nonstationary Markov Models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:wly:quante:v:11:y:2020:i:4:p:1289-1323

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