Bootstrap Improved Inference for Factor-Augmented Regressions with CCE
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/9bf1fec1-e53f-4186-ae4f-ea29ede7af96
- author
- De Vos, Ignace LU and Stauskas, Ovidijus LU
- organization
- publishing date
- 2021
- 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}}, }