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Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure

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Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure. / Kapetanios, G.; Serlenga, Laura; Shin, Yongcheol.

In: Journal of Econometrics, Vol. 220, No. 2, 01.02.2021, p. 504-531.

Research output: Contribution to journalArticlepeer-review

Harvard

Kapetanios, G, Serlenga, L & Shin, Y 2021, 'Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure', Journal of Econometrics, vol. 220, no. 2, pp. 504-531. https://doi.org/10.1016/j.jeconom.2020.04.011

APA

Kapetanios, G., Serlenga, L., & Shin, Y. (2021). Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure. Journal of Econometrics, 220(2), 504-531. https://doi.org/10.1016/j.jeconom.2020.04.011

Vancouver

Kapetanios G, Serlenga L, Shin Y. Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure. Journal of Econometrics. 2021 Feb 1;220(2):504-531. https://doi.org/10.1016/j.jeconom.2020.04.011

Author

Kapetanios, G. ; Serlenga, Laura ; Shin, Yongcheol. / Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure. In: Journal of Econometrics. 2021 ; Vol. 220, No. 2. pp. 504-531.

Bibtex - Download

@article{f6067a73dff94a9d83edcd0a02089a89,
title = "Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure",
abstract = "Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-sectional dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modified in order to account for the hierarchical factor structure in 3D panels. Further, we provide the associated asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is con…rmed by Monte Carlo simulation studies. We also demonstrate the empirical usefulness of the proposed approach through an application to a 3D panel gravity model of bilateral export flows.",
author = "G. Kapetanios and Laura Serlenga and Yongcheol Shin",
note = "{\textcopyright} 2020 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. ",
year = "2021",
month = feb,
day = "1",
doi = "10.1016/j.jeconom.2020.04.011",
language = "English",
volume = "220",
pages = "504--531",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure

AU - Kapetanios, G.

AU - Serlenga, Laura

AU - Shin, Yongcheol

N1 - © 2020 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

PY - 2021/2/1

Y1 - 2021/2/1

N2 - Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-sectional dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modified in order to account for the hierarchical factor structure in 3D panels. Further, we provide the associated asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is con…rmed by Monte Carlo simulation studies. We also demonstrate the empirical usefulness of the proposed approach through an application to a 3D panel gravity model of bilateral export flows.

AB - Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-sectional dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modified in order to account for the hierarchical factor structure in 3D panels. Further, we provide the associated asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is con…rmed by Monte Carlo simulation studies. We also demonstrate the empirical usefulness of the proposed approach through an application to a 3D panel gravity model of bilateral export flows.

U2 - 10.1016/j.jeconom.2020.04.011

DO - 10.1016/j.jeconom.2020.04.011

M3 - Article

VL - 220

SP - 504

EP - 531

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -