16,745 research outputs found
Panel Cointegration with Global Stochastic Trends
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 unobservable 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
mixed normal and permit inference to be conducted using standard test
statistics. The estimators are also valid when there are mixed stationary and
non-stationary factors, as well as when the factors are all stationary
Unit Roots and Cointegration in Panels
This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components
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