A Tractable Framework for Analyzing a Class of Nonstationary Markov Models

Lilia Maliar, Serguei Maliar, John Taylor and Inna Tsener

No 15105, Economics Working Papers from Hoover Institution, Stanford University

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.

Keywords: nonstationary models; unbalanced growth; time varying transition probabilities; time varying parameters; anticipated shock; shooting method; parameter shift; parameter drift; regime switch; stochastic volatility; capital augmenting; seasonality; Fair and Taylor; extended path; Smolyak method (search for similar items in EconPapers)
JEL-codes: C61 C63 C68 E31 E52 (search for similar items in EconPapers)
Pages: 64 pages
Date: 2015-03
New Economics Papers: this item is included in nep-ecm, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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