Exploiting Cross Section Variation for Unit Root Inference in Dynamic Data
This paper considers unit root regressions in data having simultaneously extensive cross section and time-eries variation. The standard least squares estimators in such data structures turn out to have an asymptotic distribution that is neither Dickey-Fuller, nor normal and asymptotically unbiased. Instead, the estimator turns out to be consistent and asymptotically normal, but has a nonvanishing bias in its asymptotic distribution.
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