IDEAS home Printed from https://ideas.repec.org/a/cup/etheor/v20y2004i06p1227-1260_20.html
   My bibliography  Save this article

Estimation Of The Long-Run Average Relationship In Nonstationary Panel Time Series

Author

Listed:
  • Sun, Yixiao

Abstract

This paper proposes a new class of estimators of the long-run average relationship when there is no individual time series cointegration. Using panel data with large cross section (n) and time series dimensions (T), the estimators are based on the long-run average variance estimate using bandwidth equal to T. The new estimators include the panel pooled least squares estimators and the limiting cross sectional least squares estimator as special cases. It is shown that the new estimators are consistent and asymptotically normal under both the sequential limit, wherein T goes to infinity followed by n going to infinity, and the joint limit where T and n go to infinite simultaneously. The rate condition for the joint limit to hold is relaxed to the condition that sqrt(n)/T goes to infinity, which is less restrictive than the rate condition that n/T goes to infinity, as imposed by Phillips and Moon (1999). By taking powers of the Bartlett and Parzen kernels, this paper introduces two new classes of kernels, the sharp kernels and steep kernels, and shows that these new kernels deliver new estimators of the long-run average relationship that are more efficient than the existing ones. A simulation study supports the asymptotic results.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Sun, Yixiao, 2004. "Estimation Of The Long-Run Average Relationship In Nonstationary Panel Time Series," Econometric Theory, Cambridge University Press, vol. 20(06), pages 1227-1260, December.
  • Handle: RePEc:cup:etheor:v:20:y:2004:i:06:p:1227-1260_20
    as

    Download full text from publisher

    File URL: http://journals.cambridge.org/abstract_S0266466604206077
    File Function: link to article abstract page
    Download Restriction: no

    Other versions of this item:

    Citations

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


    Cited by:

    1. Nguyen-Van, Phu, 2010. "Energy consumption and income: A semiparametric panel data analysis," Energy Economics, Elsevier, vol. 32(3), pages 557-563, May.
    2. Sun, Yixiao & Phillips, Peter C.B. & Jin, Sainan, 2011. "Power Maximization And Size Control In Heteroskedasticity And Autocorrelation Robust Tests With Exponentiated Kernels," Econometric Theory, Cambridge University Press, vol. 27(06), pages 1320-1368, December.
    3. Trapani, Lorenzo, 2012. "On the asymptotic t-test for large nonstationary panel models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3286-3306.
    4. Peter C.B. Phillips & Yixiao Sun & Sainan Jin, 2005. "Improved HAR Inference," Cowles Foundation Discussion Papers 1513, Cowles Foundation for Research in Economics, Yale University.

    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:cup:etheor:v:20:y:2004:i:06:p:1227-1260_20. 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: (Keith Waters). General contact details of provider: http://journals.cambridge.org/jid_ECT .

    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 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.

    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.