Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation and Pruning-Reference-Cited by-同舟云学术

Tractable Latent State Filtering for Non-Linear DSGE Models Using a Second-Order Approximation and Pruning

Published:2014-02-01 Issue:2 Volume:45 Page:239-260
ISSN:0927-7099
Container-title:Computational Economics
language:en
Short-container-title:Comput Econ

Author:

Kollmann Robert

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)

Link

http://link.springer.com/content/pdf/10.1007/s10614-013-9418-3.pdf

Reference23 articles.

1. An, S., & Schorfheide, F. (2007). Bayesian analysis of DSGE models. Econometric Reviews, 26, 113–172.

2. Andreasen, M. (2012). Non-linear DSGE models and the central difference Kalman filter. Journal of Applied Econometrics, 28, 929–955.

3. Andreasen, M., Fernández-Villaverde, J., & Rubio-Ramírez, J. (2013). The pruned state-space system for non-linear DSGE models: Theory and empirical applications. NBER Working Paper No. 18983.

4. Aruoba, S., Bocola, L., & Schorfheide, F. (2012). A new class of nonlinear time series models for the evaluation of DSGE models. Working Paper, University of Maryland and University of Pennsylvania.

5. Collard, F., & Juillard, M. (2001). Perturbation methods for rational expectations models. Working Paper, GREMAQ and CEPREMAP.

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