IDEAS home Printed from https://ideas.repec.org/p/hhs/rbnkwp/0224.html
   My bibliography  Save this paper

Block Kalman filtering for large-scale DSGE models

Author

Listed:
  • Strid, Ingvar

    (Stockholm School of Economics)

  • Walentin, Karl

    (Research Department, Central Bank of Sweden)

Abstract

In this paper block Kalman filters for Dynamic Stochastic General Equilibrium models are presented and evaluated. Our approach is based on the simple idea of writing down the Kalman filter recursions on block form and appropriately sequencing the operations of the prediction step of the algorithm. It is argued that block filtering is the only viable serial algorithmic approach to significantly reduce Kalman filtering time in the context of large DSGE models. For the largest model we evaluate the block filter reduces the computation time by roughly a factor 2. Block filtering compares favourably with the more general method for faster Kalman filtering outlined by Koopman and Durbin (2000) and, furthermore, the two approaches are largely complementary

Suggested Citation

  • Strid, Ingvar & Walentin, Karl, 2008. "Block Kalman filtering for large-scale DSGE models," Working Paper Series 224, Sveriges Riksbank (Central Bank of Sweden).
  • Handle: RePEc:hhs:rbnkwp:0224
    as

    Download full text from publisher

    File URL: http://www.riksbank.com/upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2008/wp224ny.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Adolfson, Malin & Laseen, Stefan & Linde, Jesper & Villani, Mattias, 2007. "Bayesian estimation of an open economy DSGE model with incomplete pass-through," Journal of International Economics, Elsevier, vol. 72(2), pages 481-511, July.
    2. S. J. Koopman & J. Durbin, 2000. "Fast Filtering and Smoothing for Multivariate State Space Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(3), pages 281-296, May.
    3. Christoffel, Kai & Coenen, Gunter & Warne, Anders, 2007. "Conditional versus unconditional forecasting with the New Area-Wide Model of the euro area," MPRA Paper 76759, University Library of Munich, Germany.
    4. Ivano Azzini & Riccardo Girardi & Marco Ratto, 2007. "Parallelization of Matlab codes under Windows platform for Bayesian estimation: A Dynare application," Working Papers 1, Euro-area Economy Modelling Centre.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Edward Herbst, 2015. "Using the “Chandrasekhar Recursions” for Likelihood Evaluation of DSGE Models," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 693-705, April.
    2. Strid, Ingvar, 2008. "Metropolis-Hastings prefetching algorithms," SSE/EFI Working Paper Series in Economics and Finance 706, Stockholm School of Economics, revised 02 Dec 2009.
    3. Sebastian Ankargren & Paulina Jon'eus, 2019. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Papers 1907.01075, arXiv.org.
    4. Sanha Noh, 2020. "Posterior Inference on Parameters in a Nonlinear DSGE Model via Gaussian-Based Filters," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 795-841, December.
    5. Strid, Ingvar, 2010. "Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2814-2835, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pablo Burriel & Jesús Fernández-Villaverde & Juan Rubio-Ramírez, 2010. "MEDEA: a DSGE model for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 1(1), pages 175-243, March.
    2. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2008. "How Structural Are Structural Parameters?," NBER Chapters, in: NBER Macroeconomics Annual 2007, Volume 22, pages 83-137, National Bureau of Economic Research, Inc.
    3. Barbara Rudolf & Mathias Zurlinden, 2014. "A compact open economy DSGE model for Switzerland," Economic Studies 2014-08, Swiss National Bank.
    4. Tovar, Camilo Ernesto, 2009. "DSGE Models and Central Banks," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 3, pages 1-31.
    5. Ian Christensen & Paul Corrigan & Caterina Mendicino & Shin‐Ichi Nishiyama, 2016. "Consumption, housing collateral and the Canadian business cycle," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(1), pages 207-236, February.
    6. Rodríguez, Aldo, 2020. "Estimación Bayesiana de un Modelo de Economía Abierta con Sector Bancario," Dynare Working Papers 52, CEPREMAP.
    7. Adnan Haider Bukhari & Safdar Ullah Khan, 2008. "A Small Open Economy DSGE Model for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 47(4), pages 963-1008.
    8. Morita, Hiroshi, 2014. "External shocks and Japanese business cycles: Evidence from a sign-restricted VAR model," Japan and the World Economy, Elsevier, vol. 30(C), pages 59-74.
    9. Javier Andrés & Pablo Burriel & Ángel Estrada, 2006. "BEMOD: a DSGE model for the Spanish economy and the rest of the Euro area," Working Papers 0631, Banco de España.
    10. Forni, L. & Gerali, A. & Notarpietro, A. & Pisani, M., 2015. "Euro area, oil and global shocks: An empirical model-based analysis," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 295-314.
    11. Stefan Ried, 2009. "Putting Up a Good Fight: The Galí-Monacelli Model versus “The Six Major Puzzles in International Macroeconomicsâ€," SFB 649 Discussion Papers SFB649DP2009-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    12. Eyal Argov & Emanuel Barnea & Alon Binyamini & Eliezer Borenstein & David Elkayam & Irit Rozenshtrom, 2012. "MOISE: A DSGE Model for the Israeli Economy," Bank of Israel Working Papers 2012.06, Bank of Israel.
    13. Rabanal, Pau & Rubio-Ramírez, Juan F., 2015. "Can international macroeconomic models explain low-frequency movements of real exchange rates?," Journal of International Economics, Elsevier, vol. 96(1), pages 199-211.
    14. Romain Houssa & Jolan Mohimont & Chris Otrok, 2019. "A model for international spillovers to emerging markets," Working Paper Research 370, National Bank of Belgium.
    15. Cúrdia, Vasco & Finocchiaro, Daria, 2013. "Monetary regime change and business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 37(4), pages 756-773.
    16. John B. Taylor & Volker Wieland, 2012. "Surprising Comparative Properties of Monetary Models: Results from a New Model Database," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 800-816, August.
    17. D. Siena, 2014. "The European Monetary Union and Imbalances: Is it an Anticipation Story ?," Working papers 501, Banque de France.
    18. Hansen, James & Gross, Isaac, 2018. "Commodity price volatility with endogenous natural resources," European Economic Review, Elsevier, vol. 101(C), pages 157-180.
    19. Queijo, Virginia, 2005. "How Important are Financial Frictions in the U.S. and Euro Area?," Seminar Papers 738, Stockholm University, Institute for International Economic Studies.
    20. Adolfson, Malin & Laseen, Stefan & Linde, Jesper & Villani, Mattias, 2007. "Bayesian estimation of an open economy DSGE model with incomplete pass-through," Journal of International Economics, Elsevier, vol. 72(2), pages 481-511, July.

    More about this item

    Keywords

    Kalman filter; DSGE model; Bayesian estimation; Computational speed; Algorithm; Fortran; Matlab;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:rbnkwp:0224. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lena Löfgren). General contact details of provider: https://edirc.repec.org/data/rbgovse.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.