Solving Stochastic Dynamic Programming Problems: A Mixed Complementarity Approach

被引：2
|
作者：
Chang, Wonjun [1 ]
Ferris, Michael C. [4 ,5 ]
Kim, Youngdae [2 ]
Rutherford, Thomas F. [3 ,5 ]
机构：
[1] CRA Int, Washington, DC 20004 USA
[2] Argonne Natl Lab, Math & Comp Sci Div, Lemont, IL USA
[3] Univ Wisconsin, Dept Agr & Appl Econ, Madison, WI USA
[4] Univ Wisconsin, Dept Comp Sci, 1210 W Dayton St, Madison, WI 53706 USA
[5] Wisconsin Inst Discovery, Optimizat Grp, Madison, WI USA
来源：
COMPUTATIONAL ECONOMICS | 2020年 / 55卷 / 03期
关键词：
Dynamic Programming; Computable general equilibrium; Complementarity; Computational methods; EQUILIBRIUM; MODELS; GAMS;
D O I：
10.1007/s10614-019-09921-y
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
We present a mixed complementarity problem (MCP) formulation of continuous state dynamic programming problems (DP-MCP). We write the solution to projection methods in value function iteration (VFI) as a joint set of optimality conditions that characterize maximization of the Bellman equation; and approximation of the value function. The MCP approach replaces the iterative component of projection based VFI with a one-shot solution to a square system of complementary conditions. We provide three numerical examples to illustrate our approach.
引用
收藏
页码：925 / 955
页数：31
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