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Bootstrap Improved Inference for Factor-Augmented Regressions with CCE

De Vos, Ignace LU and Stauskas, Ovidijus LU (2021) In Working Papers
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... (More)
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. (Less)
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author
and
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
keywords
Panel data, CCE, Bootstrap, Pairs, Factors, Bias Correction, C12, C23, C33
in
Working Papers
issue
2021:16
pages
161 pages
language
English
LU publication?
yes
id
9bf1fec1-e53f-4186-ae4f-ea29ede7af96
date added to LUP
2021-12-15 09:05:17
date last changed
2024-03-14 14:54:28
@misc{9bf1fec1-e53f-4186-ae4f-ea29ede7af96,
  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.}},
  author       = {{De Vos, Ignace and Stauskas, Ovidijus}},
  keywords     = {{Panel data; CCE; Bootstrap; Pairs; Factors; Bias Correction; C12; C23; C33}},
  language     = {{eng}},
  note         = {{Working Paper}},
  number       = {{2021:16}},
  series       = {{Working Papers}},
  title        = {{Bootstrap Improved Inference for Factor-Augmented Regressions with CCE}},
  url          = {{https://lup.lub.lu.se/search/files/177122655/WP21_16.pdf}},
  year         = {{2021}},
}