Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01t148fk501
 Title: Structured Approximate Dynamic Programming for Simulating Heterogeneous Agents in Incomplete Markets Authors: Chen, Erick Advisors: Powell, Warren Department: Operations Research and Financial Engineering Class Year: 2015 Abstract: The increasing use of dynamic stochastic general equilibrium models by central banks to conduct policy analysis necessitates the development of new algorithmic techniques capable of solving these problems. This thesis uses structured approximate dynamic programming to solve the Krusell-Smith model, a prototypical dynamic stochastic general equilibrium model that simulates heterogeneous agents in incomplete markets. We formulate the Krusell-Smith model as a stochastic programming problem and solve it using two di erent structure enforcing algorithms. In the process, we also develop a new structured approximate dynamic programming algorithm we call ADPLP. We compare performance of our approximate solutions relative to a benchmark solution and show that both our algorithms have good accuracy and convergence for small time horizons. We close by conducting sensitivity analysis policy studies using the Krusell-Smith model to simulate the e ects of the European Central Bank's recent monetary policy. Extent: 89 pages URI: http://arks.princeton.edu/ark:/88435/dsp01t148fk501 Type of Material: Princeton University Senior Theses Language: en_US Appears in Collections: Operations Research and Financial Engineering, 2000-2016

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