All Rights Reserved
AccessEcon LLC 2006, 2008.
Powered by MinhViet JSC

Inna Tsener
''A geometric programming approach to dynamic economic models''
( 2020, Vol. 40 No.2 )
Geometric programming (GP) has several attractive features: it is tractable in large-scale problems, requires no initial guess or tuning of solver parameters, guarantees the convergence to a global optimum and can deal with kinks. In this note, I argue that GP is a potentially promising tool in economics. First, I show that a stylized finite-horizon growth model can be mapped into a GP format by using simple transformations. Second, I show that GP methods produce accurate and reliable solutions including the case of occasionally binding constraints which cannot be easily treated with conventional solvers. Examples of MATLAB codes are provided.
Keywords: dynamic optimization, geometric programming, finite horizon, occasionally binding constraints, condensation
JEL: C6 - Mathematical Methods and Programming: General
D9 - Intertemporal Choice and Growth: General
Manuscript Received : Jan 18 2019 Manuscript Accepted : Apr 17 2020

  This abstract has been downloaded 916 times                The Full PDF of this paper has been downloaded 154297 times