A tractable framework for analyzing a class of nonstationary Markov models
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...
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| Veröffentlicht in: | Quantitative economics 2020-11, Vol.11 (4), p.1289-1323 |
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| creator | Maliar, Lilia Maliar, Serguei Taylor, John B Tsener, Inna |
| description | 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. |
| doi_str_mv | 10.3982/QE1360 |
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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. 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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. 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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.</abstract><cop>New Haven, CT</cop><pub>The Econometric Society</pub><doi>10.3982/QE1360</doi><tpages>35</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | anticipated shock Approximation C61 C63 C68 E31 E52 Econometrics Economic models Election results extended path Fair and Taylor method Growth models Monetary unions News nonstationary models parameter drift parameter shift Presidential elections Random variables regime switches seasonal adjustments semi-Markov models stochastic volatility technological progress time-inhomogeneous models time-varying parameters Toll roads Trends Turnpike theorem unbalanced growth Utility functions |
| title | A tractable framework for analyzing a class of nonstationary Markov models |
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