Panel Cointegration with Global Stochastic Trends
Jushan Bai (),
Chihwa Kao () and
Serena Ng ()
No 90, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University
This paper studies estimation of panel cointegration models with cross-sectional dependence generated by unobserved global stochastic trends. The standard least squares estimator is, in general, inconsistent owing to the spuriousness induced by the unobservabla I(1) trends. We propose two iterative procedures that jointly estimate the slope parameters and the stochastic trends. The resulting estimators are referred to respectively as CupBC (continuously updated and bias-corrected) and the CupFM (continuously updated and fully modified) estimators. We establish their consistency and derive their limiting distributions. Both are asymptotically unbiased and asymptotically normal and permit inference to be conducted using standard test statistics. The estimates are also valid when there are mixed stationary and non-stationary factors, as well as when the factors are all stationary.
JEL-codes: C13 C33 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Journal Article: Panel cointegration with global stochastic trends (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:90
Access Statistics for this paper
More papers in Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University 426 Eggers Hall, Syracuse, New York USA 13244-1020. Contact information at EDIRC.
Bibliographic data for series maintained by Margaret Austin ().