Working Paper: NBER ID: w21155
Authors: Lilia Maliar; Serguei Maliar; John Taylor; Inna Tsener
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 Markov models; dynamic economic models; EFP framework; calibration; simulation
JEL Codes: C61; C63; C68; E31; E52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| time-varying parameters (C32) | outcomes of the economic models (E10) |
| EFP methodology (C51) | improved accuracy in decision-making (D91) |
| anticipated regime switches and parameter drifting (C22) | economic fluctuations (E32) |
| EFP framework (E17) | calibrate and estimate parameters in unbalanced growth models (C51) |
| numerical methods (C60) | more accurate approximation of decision functions in nonstationary settings (C32) |