A Tractable Framework for Analyzing a Class of Nonstationary Markov Models

Lilia Maliar, Serguei Maliar, John Taylor and Inna Tsener

No 21155, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We study a class of infinite-horizon nonlinear dynamic economic models in which preferences, technology and laws of motion for exogenous variables can change over time either deterministically or stochastically, according to a Markov process with time-varying transition probabilities, or both. The studied models are nonstationary in the sense that the decision and value functions are time-dependent, and they cannot be generally solved by conventional solution methods. We introduce a quantitative framework, called extended function path (EFP), for calibrating, solving, simulating and estimating such models. We apply EFP to analyze a collection of challenging applications that do not admit stationary Markov equilibria, including growth models with anticipated parameters shifts and drifts, unbalanced growth under capital augmenting technological progress, anticipated regime switches, deterministically time-varying volatility and seasonal fluctuations. Also, we show an example of estimation and calibration of parameters in an unbalanced growth model using data on the U.S. economy. Examples of MATLAB code are provided.

JEL-codes: C61 C63 C68 E31 E52 (search for similar items in EconPapers)
Date: 2015-05
New Economics Papers: this item is included in nep-mac and nep-ore
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Citations: View citations in EconPapers (19)

Published as Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2020. "A tractable framework for analyzing a class of nonstationary Markov models," Quantitative Economics, Econometric Society, vol. 11(4), pages 1289-1323, November.

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Working Paper: A Tractable Framework for Analyzing a Class of Nonstationary Markov Models (2015)
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