Affiliation:
1. Department of Economics, Graduate Center, CUNY
2. CEPR
3. Department of Economics, Santa Clara University
4. Hoover Institution, Stanford University
5. NBER
6. Department of Applied Economics, University of the Balearic Islands
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.
Funder
National Science Foundation
Subject
Economics and Econometrics
Cited by
3 articles.
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