### Abstract

Language | English |
---|---|

Journal | Journal of Applied Econometrics |

DOIs | |

State | Accepted/In press - 30 Sep 2018 |

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### Keywords

- factor-augmented panel regressions
- cross-sectional dependence
- CCE

### Cite this

*Journal of Applied Econometrics*. DOI: 10.1002/jae.2661

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*Journal of Applied Econometrics*. DOI: 10.1002/jae.2661

**CCE Estimation of Factor‐Augmented Regression Models with more Factors than Observables.** / Karabiyik, H.; Urbain, J.R.Y.J.; Westerlund, Joakim.

Research output: Contribution to Journal › Article › Academic › peer-review

TY - JOUR

T1 - CCE Estimation of Factor‐Augmented Regression Models with more Factors than Observables

AU - Karabiyik,H.

AU - Urbain,J.R.Y.J.

AU - Westerlund,Joakim

PY - 2018/9/30

Y1 - 2018/9/30

N2 - This paper considers estimation of factor‐augmented panel data regression models. One of the most popular approaches towards this end is the common correlated effects (CCE) estimator of Pesaran (Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 96 –1012, 2006). For the pooled version of this estimator to be consistent, either the number of observables must be larger than the number of unobserved common factors, or the factor loadings must be distributed independently of each other. This is a problem in the typical application involving only a small number of regressors and/or correlated loadings. The current paper proposes a simple extension to the CCE procedure by which both requirements can be relaxed. The CCE approach is based on taking the cross‐section average of the observables as an estimator of the common factors. The idea put forth in the current paper is to consider not only the average but also other cross‐section combinations. Asymptotic properties of the resulting combination‐augmented CCE (C3E) estimator are provided and tested in small samples using both simulated and real data.

AB - This paper considers estimation of factor‐augmented panel data regression models. One of the most popular approaches towards this end is the common correlated effects (CCE) estimator of Pesaran (Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 96 –1012, 2006). For the pooled version of this estimator to be consistent, either the number of observables must be larger than the number of unobserved common factors, or the factor loadings must be distributed independently of each other. This is a problem in the typical application involving only a small number of regressors and/or correlated loadings. The current paper proposes a simple extension to the CCE procedure by which both requirements can be relaxed. The CCE approach is based on taking the cross‐section average of the observables as an estimator of the common factors. The idea put forth in the current paper is to consider not only the average but also other cross‐section combinations. Asymptotic properties of the resulting combination‐augmented CCE (C3E) estimator are provided and tested in small samples using both simulated and real data.

KW - factor-augmented panel regressions

KW - cross-sectional dependence

KW - CCE

U2 - 10.1002/jae.2661

DO - 10.1002/jae.2661

M3 - Article

JO - Journal of Applied Econometrics

T2 - Journal of Applied Econometrics

JF - Journal of Applied Econometrics

SN - 0883-7252

ER -