IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v232y2023ics0165176523003828.html
   My bibliography  Save this article

Impulse response function analysis for Markov switching var models

Author

Listed:
  • Cavicchioli, Maddalena

Abstract

We exactly derive the regime-dependent impulse response functions for a Markov switching vector autoregression (VAR) model in terms of neat matrix expressions in closed form. The key is to recognize that the latent first-order Markov switching process in the model has a VAR(1) representation, and that the model can be cast into a state-space form. Using such a representation, the regime-dependent impulse response function analysis can be processed with respect to either an asymmetric discrete shock or to a symmetric continuous shock. Our results extend and correct those obtained by Ehrmann et al. (2003) and coincide with those by Hamilton (1994) for the case of standard VAR models.

Suggested Citation

  • Cavicchioli, Maddalena, 2023. "Impulse response function analysis for Markov switching var models," Economics Letters, Elsevier, vol. 232(C).
  • Handle: RePEc:eee:ecolet:v:232:y:2023:i:c:s0165176523003828
    DOI: 10.1016/j.econlet.2023.111357
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176523003828
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2023.111357?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Markov switching; Vector autoregression; Impulse response function; State-space representation;
    All these keywords.

    JEL classification:

    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:eee:ecolet:v:232:y:2023:i:c:s0165176523003828. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.