Bootstrap Improved Inference for Factor-Augmented Regressions with CCE

Ignace De Vos and Ovidijus Stauskas ()
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Ovidijus Stauskas: Department of Economics, Lund University, Postal: School of Economics and Management, Box 7080, S-220 07 Lund, Sweden

No 2021:16, Working Papers from Lund University, Department of Economics

Abstract: 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.

Keywords: Panel data; CCE; Bootstrap; Pairs; Factors; Bias Correction (search for similar items in EconPapers)
JEL-codes: C12 C23 C33 (search for similar items in EconPapers)
Pages: 161 pages
Date: 2021-11-19
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:lunewp:2021_016

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