IDEAS home Printed from https://ideas.repec.org/p/bde/wpaper/9405.html
   My bibliography  Save this paper

BVAR models in the context of cointegration: A Monte Carlo experiment

Author

Listed:
  • Luis J. Álvarez

    (Banco de España)

  • Fernando C. Ballabriga

    (ESADE)

Abstract

The kind of prior typically employed in Bayesian vector autoregression (BVAR) analysis has aroused widespread suspicion about the ability of these models to capture long-run patterns. This paper specifies a bivariate cointegrated stochastic process and conducts a Monte Carlo experiment to assess the small sample performance of two classical and two Bayesian estimation methods commonly applied to VAR models. In addition, a proposal to introduce a new dimension to the prior information in order to allow for explicit account of long-run restrictions is suggested and evaluated in the light of the experiment. The results of the experiment show that: the Minnesota -type prior with hyperparameter search performs well, suggesting that the prevalent suspicion about the inability of this prior to capture long-run patterns is not well-grounded; the fine-tunning of the prior is crucial; and adding long-run restrictions to the prior does not provide improvements in the case analyzed.

Suggested Citation

  • Luis J. Álvarez & Fernando C. Ballabriga, 1994. "BVAR models in the context of cointegration: A Monte Carlo experiment," Working Papers 9405, Banco de España.
    • Handle: RePEc:bde:wpaper:9405
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Florian Huber & Jesus Crespo-Cuaresma & Martin Feldkircher, 2014. "Forecasting with Bayesian Global Vector Autoregressions," ERSA conference papers ersa14p25, European Regional Science Association.
    2. Sonsoles Castillo & Fernando C. Ballabriga, 2003. "BBVA-ARIES: a forecasting and simulation model for EMU," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 411-426.
    3. Andrea Nobili, 2005. "Forecasting Output Growth And Inflation In The Euro Area: Are Financial Spreads Useful?," Temi di discussione (Economic working papers) 544, Bank of Italy, Economic Research and International Relations Area.
    4. Enrique M. Quilis(1), "undated". "Modelos Bvar: Especificación, Estimación E Inferencia," Working Papers 8-02 Classification-JEL :, Instituto de Estudios Fiscales.
    5. Mr. Matteo Ciccarelli & Mr. Alessandro Rebucci, 2003. "Bayesian Vars: A Survey of the Recent Literature with An Application to the European Monetary System," IMF Working Papers 2003/102, International Monetary Fund.
    6. Ballabriga, Fernando & Sebastian, Miguel & Valles, Javier, 1999. "European asymmetries," Journal of International Economics, Elsevier, vol. 48(2), pages 233-253, August.
    7. Jesús Crespo Cuaresma & Martin Feldkircher & Florian Huber, 2014. "Forecasting with Bayesian Global Vector Autoregressive Models: A Comparison of Priors," Working Papers 189, Oesterreichische Nationalbank (Austrian Central Bank).
    8. Luís Catela Nunes, 2003. "Forecasting Euro Area Aggregates with Bayesian VAR and VECM Models," Working Papers w200304, Banco de Portugal, Economics and Research Department.

    More about this item

    Keywords

    Bayesian vector autoregression; cointegration; Monte Carlo experiment;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    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:bde:wpaper:9405. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ángel Rodríguez. Electronic Dissemination of Information Unit. Research Department. Banco de España (email available below). General contact details of provider: https://edirc.repec.org/data/bdegves.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.