Abstract
In this paper, we focus on estimating the degree of cross-sectional dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of cross-sectional dependence denoted by α, which is based on the number of non-zero pair-wise cross correlations of these errors. We prove that our estimator, α~ , is consistent and derive the rate at which it approaches its true value. We also propose a resampling procedure for the construction of confidence bounds around the estimator of α. We evaluate the finite sample properties of the proposed estimator by use of a Monte Carlo simulation study. The numerical results are encouraging and supportive of the theoretical findings. Finally, we undertake an empirical investigation of α for the errors of the CAPM model and its Fama-French extensions using 10-year rolling samples from S&P 500 securities over the period Sept 1989 - May 2018.
| Original language | English |
|---|---|
| Number of pages | 57 |
| Journal | Sankhya B |
| DOIs | |
| Publication status | Accepted/In press - 2019 |
Keywords
- CAPM and Fama-French factors
- Cross-sectional averages
- Cross-sectional dependence
- Pair-wise correlations
- Weak and strong factor models
Cite this
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Exponent of cross-sectional dependence for residuals. / Bailey, Natalia; Kapetanios, George; Pesaran, M. Hashem.
In: Sankhya B, 2019.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Exponent of cross-sectional dependence for residuals
AU - Bailey, Natalia
AU - Kapetanios, George
AU - Pesaran, M. Hashem
PY - 2019
Y1 - 2019
N2 - In this paper, we focus on estimating the degree of cross-sectional dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of cross-sectional dependence denoted by α, which is based on the number of non-zero pair-wise cross correlations of these errors. We prove that our estimator, α~ , is consistent and derive the rate at which it approaches its true value. We also propose a resampling procedure for the construction of confidence bounds around the estimator of α. We evaluate the finite sample properties of the proposed estimator by use of a Monte Carlo simulation study. The numerical results are encouraging and supportive of the theoretical findings. Finally, we undertake an empirical investigation of α for the errors of the CAPM model and its Fama-French extensions using 10-year rolling samples from S&P 500 securities over the period Sept 1989 - May 2018.
AB - In this paper, we focus on estimating the degree of cross-sectional dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of cross-sectional dependence denoted by α, which is based on the number of non-zero pair-wise cross correlations of these errors. We prove that our estimator, α~ , is consistent and derive the rate at which it approaches its true value. We also propose a resampling procedure for the construction of confidence bounds around the estimator of α. We evaluate the finite sample properties of the proposed estimator by use of a Monte Carlo simulation study. The numerical results are encouraging and supportive of the theoretical findings. Finally, we undertake an empirical investigation of α for the errors of the CAPM model and its Fama-French extensions using 10-year rolling samples from S&P 500 securities over the period Sept 1989 - May 2018.
KW - CAPM and Fama-French factors
KW - Cross-sectional averages
KW - Cross-sectional dependence
KW - Pair-wise correlations
KW - Weak and strong factor models
UR - http://www.scopus.com/inward/record.url?scp=85066628244&partnerID=8YFLogxK
U2 - 10.1007/s13571-019-00196-9
DO - 10.1007/s13571-019-00196-9
M3 - Article
JO - Sankhya B
JF - Sankhya B
SN - 0976-8386
ER -