Merging simulation and projection approaches to solve high‐dimensional problems with an application to a new Keynesian model

Lilia Maliar and Serguei Maliar

Quantitative Economics, 2015, vol. 6, issue 1, 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.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (61)

Downloads: (external link)
http://hdl.handle.net/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:quante:v:6:y:2015:i:1:p:1-47

Ordering information: This journal article can be ordered from
https://www.econometricsociety.org/membership

Access Statistics for this article

More articles in Quantitative Economics from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().