IDEAS home Printed from https://ideas.repec.org/p/sin/wpaper/08-a001.html
   My bibliography  Save this paper

The Long Memory Autoregressive Distributed Lag Model and Its Application on Congressional Approval

Author

Abstract

This paper considers the instrumental variables (IV) estimation of the autoregressive distributed lag (ADL) model consisting of fractionally integrated regressors and disturbance term, while allowing for part of the regressors to be endogenous. The idea of Liviatan (1963) and that of Tsay (2007) are combined to construct consistent and asymptotically normally distributed multipledifferenced two-stage-least-squares (MD-TSLS) and MD generalized method of moments (MD-GMM) estimators for the long memory ADL model. The simulations show that the performance of the MD-GMM estimator is especially excellent even though the sample size is 100. The IV estimators are applied to the data of Durr, Gilmour, and Wolbrecht (1997) about Congressional approval. As compared to the 0.08 estimate of the long-run effect of presidential approval on Congressional approval based on the scalar ADL model of De Boef and Keele (2008), a stronger support for the divided party government hypothesis is found for a class of the vector ADL model which generates a corresponding long-run impact equal to 0.26 or higher.

Suggested Citation

  • Wen-Jen Tsay, 2008. "The Long Memory Autoregressive Distributed Lag Model and Its Application on Congressional Approval," IEAS Working Paper : academic research 08-A001, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    • Handle: RePEc:sin:wpaper:08-a001
    as

    Download full text from publisher

    File URL: https://www.econ.sinica.edu.tw/~econ/pdfPaper/08-A001.pdf
    Download Restriction: no
    ---><---

    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:sin:wpaper:08-a001. 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: HsiaoyunLiu (email available below). General contact details of provider: https://edirc.repec.org/data/sinictw.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.