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SSRN Electronic Journal
Article . 2010 . Peer-reviewed
Data sources: Crossref
EconStor
Research . 2010
Data sources: EconStor
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On Approximating DSGE Models by Series Expansions

Authors: Lombardo, Giovanni;

On Approximating DSGE Models by Series Expansions

Abstract

We show how to use a simple perturbation method to solve non-linear rational expectation models. Drawing from the applied mathematics literature we propose a method consisting of series expansions of the non-linear system around a known solution. The variables are represented in terms of their orders of approximation with respect to a perturbation parameter. The final solution, therefore, is the sum of the different orders. This approach links to formal arguments the idea that each order of approximation is solved recursively taking as given the lower order of approximation. Therefore, this method is not subject to the ambiguity concerning the order of the variables in the resulting state-space representation as, for example, has been discussed by Kim et al. (2008). Provided that the model is locally stable, the approximation technique discussed in this paper delivers stable solutions at any order of approximation.

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Keywords

Dynamisches Gleichgewicht, Nichtlineare Dynamik, Non-linear difference equations, Perturbation methods, Series expansions, Solving dynamic stochastic general equilibrium models, ddc:330, Iteratives Verfahren, Perturbation methods, Series expansions, E0, C63, Non-linear difference equations, Rationale Erwartung, Solving dynamic stochastic general equilibrium models, Theorie

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Average
Top 10%
Top 10%
bronze