IDEAS home Printed from https://ideas.repec.org/h/spr/sptchp/978-981-95-7226-7_12.html

Cointegration, Error Correction, and Vector Autoregression

In: Econometrics in Theory and Practice

Author

Listed:
  • Panchanan Das

    (University of Calcutta, Department of Economics)

Abstract

A regression model with unit root variables may provide spurious results. The random error of a regression model should be white noise for robust results. White noise error implies disequilibrium is a temporary phenomenon. Although the distributional assumptions of random error imply its white noise behaviour, the time series variables used in a regression model which are themselves random may distort the white noise behaviour of the error term. The concept of cointegration put forward by Clive Granger resolves the problem of getting robust result in the presence of unit root in the variables used in a regression model. To understand the nature of dynamics, all variables used in a regression model may be considered as endogenous that generates a system of equations in a regression model in vector form. Vector autoregression is appropriate when the theory fails to provide the direction of causality uniquely. This chapter deals with these problems of a regression model with time series data.

Suggested Citation

  • Panchanan Das, 2026. "Cointegration, Error Correction, and Vector Autoregression," Springer Texts in Business and Economics, in: Econometrics in Theory and Practice, edition 0, chapter 12, pages 429-491, Springer.
    • Handle: RePEc:spr:sptchp:978-981-95-7226-7_12
    DOI: 10.1007/978-981-95-7226-7_12
    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
    for a similarly titled item that would be available.

    More about this item

    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:spr:sptchp:978-981-95-7226-7_12. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.