EconPapers has moved to http://EconPapers.repec.org! Please update your bookmarks.
A note on the estimation of long-run relationships in panel equations with cross-section linkages
Francesca Di Iorio ()
Stefano Fachin , 2012, vol. 6, issue 20, pages 1-18
Economics - The Open-Access, Open-Assessment E-Journal Abstract:
The authors address the issue of estimation and inference in dependent non-stationary panels of small cross-section dimensions. The main conclusion is that the best results are obtained applying bootstrap inference to single-equation estimators, such as fully modified ordinary least squares and dynamic ordinary least squares. Seemingly unrelated regression estimators perform badly, or are even unfeasible, when the time dimension is not very large compared to the cross-section dimension. --
Keywords: panel cointegration; fully modified ordinary least squares; fully modified seemingly unrelated regression; dynamic ordinary least squares; dynamic seemingly unrelated regression (search for similar items in EconPapers)
JEL-codes: C15 C23 C33 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc Citations View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link) http://dx.doi.org/10.5018/economics-ejournal.ja.2012-20 http://econstor.eu/bitstream/10419/59035/1/717296768.pdf (application/pdf)
Related works: Working Paper: A note on the estimation of long-run relationships in panel equations with cross-section linkages (2012) This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:zbw:ifweej:201220
Access Statistics for this article
Economics - The Open-Access, Open-Assessment E-Journal is currently edited by
Dennis J. Snower
More articles in Economics - The Open-Access, Open-Assessment E-Journal from Kiel Institute for the World Economy
Contact information at EDIRC. Series data maintained by ZBW - German National Library of Economics ().