A geometric programming approach to dynamic economic models

Inna Tsener

Economics Bulletin, 2020, vol. 40, issue 2, 1068-1074

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C6 D9 (search for similar items in EconPapers)
Date: 2020-04-17
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