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Using nonlinear model predictive control for dynamic decision problems in economics

URN to cite this document: urn:nbn:de:bvb:703-epub-1855-4

Title data

Grüne, Lars ; Semmler, Willi ; Stieler, Marleen:
Using nonlinear model predictive control for dynamic decision problems in economics.
Department of Mathematics, University of Bayreuth
Bayreuth , 2015 . - 32 S.

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basicgrowthmodel.m - Preprint
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Project information

Project title:
Project's official titleProject's id
Marie-Curie Initial Training Network "Sensitivity Analysis for Deterministic Controller Design" (SADCO)264735-SADCO
Fulbright CommissionNo information
Internationales Doktorandenkolleg "Identifikation, Optimierung und Steuerung für technische Anwendungen"K-NW-2004-143

Project financing: Andere
European Union "FP7-People-ITN" programme; Fulbright Commission; Elitenetzwerk Bayern

Abstract

This paper presents a new approach to solve dynamic decision models in economics. The proposed procedure, called Nonlinear Model Predictive Control (NMPC), relies on the iterative solution of optimal control problems on finite time horizons and is well established in engineering applications for stabilization and tracking problems. Only quite recently, extensions to more general optimal control problems including those appearing in economic applications have been investigated. Like Dynamic Programming (DP), NMPC does not rely on linearization techniques but uses the full nonlinear model and in this sense provides a global solution to the problem. However, unlike DP, NMPC only computes one optimal trajectory at a time, thus avoids to grid the state space and for this reason the computational demand grows much more moderately with the space dimension than for DP. In this paper we explain the basic idea of NMPC, give a proof concerning the accuracy of NMPC for discounted optimal control problems, present implementational details, and demonstrate the ability of NMPC to solve dynamic decision problems in economics by solving low and high dimensional examples, including models with multiple equilibria, tracking and stochastic problems.

Further data

Item Type: Preprint, postprint
Additional notes (visible to public): 2013 (first version), 2015 (substantially revised version)
Keywords: complex decision models; long and short horizon models; dynamic optimization; multiple equilibria; regime changes; NMPC
DDC Subjects: 500 Science > 510 Mathematics
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics V (Applied Mathematics) > Chair Mathematics V (Applied Mathematics) - Univ.-Prof. Dr. Lars Grüne)
Profile Fields > Advanced Fields > Nonlinear Dynamics
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics V (Applied Mathematics)
Profile Fields
Profile Fields > Advanced Fields
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-1855-4
Date Deposited: 06 Mar 2015 11:19
Last Modified: 28 Mar 2019 10:38
URI: https://epub.uni-bayreuth.de/id/eprint/1855

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