Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/260336 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
Working Paper No. 2021:16
Verlag: 
Lund University, School of Economics and Management, Department of Economics, Lund
Zusammenfassung: 
The Common Correlated Effects (CCE) methodology is now well established for the analysis of factor-augmented panel models. Yet, it is often neglected that the pooled variant is biased unless the cross-section dimension (N) of the dataset dominates the time series length (T). This is problematic for inference with typical macroeconomic datasets where T often equal or larger than N. Given that an analytical correction is also generally infeasible, the issue remains without a solution. In response, we provide in this paper the theoretical foundation for the cross-section, or pairs bootstrap in large N and T panels with T/N finite. We show that the scheme replicates the distribution of the CCE estimators, under both constant and heterogeneous slopes, such that bias can be eliminated and asymptotically correct inference can ensue even when N does not dominate. Monte Carlo experiments illustrate that the asymptotic properties also translate well to finite samples.
Schlagwörter: 
Panel data
CCE
Bootstrap
Pairs
Factors
Bias Correction
JEL: 
C12
C23
C33
Dokumentart: 
Working Paper

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