Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253592 
Year of Publication: 
2020
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 11 [Issue:] 4 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2020 [Pages:] 1289-1323
Publisher: 
The Econometric Society, New Haven, CT
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.
Subjects: 
Turnpike theorem
time-inhomogeneous models
nonstationary models
semi-Markov models
unbalanced growth
time-varying parameters
trends
anticipated shock
parameter shift
parameter drift
regime switches
stochastic volatility
technological progress
seasonal adjustments
Fair and Taylor method
extended path
JEL: 
C61
C63
C68
E31
E52
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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