Arbeitspapier
On approximating DSGE models by series expansions
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: ECB Working Paper ; No. 1264
- Klassifikation
-
Wirtschaft
Computational Techniques; Simulation Modeling
- Thema
-
Non-linear difference equations
Perturbation methods
Series expansions
Solving dynamic stochastic general equilibrium models
Nichtlineare Dynamik
Rationale Erwartung
Dynamisches Gleichgewicht
Iteratives Verfahren
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Lombardo, Giovanni
- Ereignis
-
Veröffentlichung
- (wer)
-
European Central Bank (ECB)
- (wo)
-
Frankfurt a. M.
- (wann)
-
2010
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Lombardo, Giovanni
- European Central Bank (ECB)
Entstanden
- 2010
Ähnliche Objekte (12)
Combinatorial series expansions for point processes
Approximating Perpetuities
Approximating innovation potential with neurofuzzy robust model
ifo DSGE model 2.0
DSGE model-based forecasting
The FRBNY DSGE Model
Forecasting with DSGE Models
Forecasting with DSGE models
Series Expansions for Finite-State Markov Chains
Predictive likelihood comparisons with DSGE and DSGE-VAR models
Approximating Data and Statistical Procedures - I. Approximating Data
Approximating data and statistical procedures. I. Approximating data
Combinatorial series expansions for point processes
Approximating Perpetuities
Approximating innovation potential with neurofuzzy robust model
ifo DSGE model 2.0
DSGE model-based forecasting
The FRBNY DSGE Model
Forecasting with DSGE Models
Forecasting with DSGE models
Series Expansions for Finite-State Markov Chains
Predictive likelihood comparisons with DSGE and DSGE-VAR models
Approximating Data and Statistical Procedures - I. Approximating Data
Approximating data and statistical procedures. I. Approximating data
Combinatorial series expansions for point processes
Approximating Perpetuities
Approximating innovation potential with neurofuzzy robust model
ifo DSGE model 2.0
DSGE model-based forecasting
The FRBNY DSGE Model
Forecasting with DSGE Models
Forecasting with DSGE models
Series Expansions for Finite-State Markov Chains
Predictive likelihood comparisons with DSGE and DSGE-VAR models
Approximating Data and Statistical Procedures - I. Approximating Data