Advanced Search
MyIDEAS: Login to save this article or follow this journal

Sieve bootstrap t-tests on long-run average parameters

Contents:

Author Info

  • Fuertes, Ana-Maria

Abstract

Panel estimators can provide consistent measures of a long-run average parameter even if the individual regressions are spurious. However, the t-test on this parameter is fraught with problems because the limit distribution of the test statistic is non-standard and rather complicated, particularly in panels with mixed (non-)stationary errors. A sieve bootstrap framework is suggested to approximate the distribution of the t-statistic. An extensive Monte Carlo study demonstrates that the bootstrap is quite useful in this context.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.sciencedirect.com/science/article/B6V8V-4R8WJMJ-1/1/fbff83372afa8bcd4f452198adeff104
Download Restriction: Full text for ScienceDirect subscribers only.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 52 (2008)
Issue (Month): 7 (March)
Pages: 3354-3370

as in new window
Handle: RePEc:eee:csdana:v:52:y:2008:i:7:p:3354-3370

Contact details of provider:
Web page: http://www.elsevier.com/locate/csda

Related research

Keywords:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

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

Cited by:
  1. Smeekes Stephan & Urbain Jean-Pierre, 2011. "On the Applicability of the Sieve Bootstrap in Time series Panels," Research Memorandum 055, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  2. Eberhardt, Markus & Teal, Francis, 2009. "A Common Factor Approach to Spatial Heterogeneity in Agricultural Productivity Analysis," MPRA Paper 15810, University Library of Munich, Germany.
  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. Di Iorio, Francesca & Fachin, Stefano, 2012. "A note on the estimation of long-run relationships in panel equations with cross-section linkages," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 6(20), pages 1-18.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:52:y:2008:i:7:p:3354-3370. 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: (Zhang, Lei).

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.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.