Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/150379
Authors: 
Maliar, Lilia
Maliar, Serguei
Year of Publication: 
2015
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 6 [Year:] 2015 [Issue:] 1 [Pages:] 1-47
Abstract: 
We introduce a numerical algorithm for solving dynamic economic models that merges stochastic simulation and projection approaches: we use simulation to approximate the ergodic measure of the solution, we cover the support of the constructed ergodic measure with a fixed grid, and we use projection techniques to accurately solve the model on that grid. The construction of the grid is the key novel piece of our analysis: we replace a large cloud of simulated points with a small set of “representative” points. We present three alternative techniques for constructing representative points: a clustering method, an ε-distinguishable set method, and a locally-adaptive variant of the ε-distinguishable set method. As an illustration, we solve one- and multi-agent neoclassical growth models and a large-scale new Keynesian model with a zero lower bound on nominal interest rates. The proposed solution algorithm is tractable in problems with high dimensionality (hundreds of state variables) on a desktop computer.
Subjects: 
Ergodic set
distinguishable set
clusters
adaptive grid
discrepancy
large-scale model
new Keynesian model
ZLB
stochastic simulation
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/3.0/
Document Type: 
Article

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