Inference on factor structures in heterogeneous panels

This paper develops an estimation and testing framework for a stationary large panel model with observable regressors and unobservable common factors. We allow for slope heterogeneity and for correlation between the common factors and the regressors. We propose a two stage estimation procedure for t...

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Main Authors: Castagnetti, Carolina, Rossi, Eduardo, Trapani, Lorenzo
Format: Article
Published: Elsevier 2015
Subjects:
Online Access:https://eprints.nottingham.ac.uk/46943/
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author Castagnetti, Carolina
Rossi, Eduardo
Trapani, Lorenzo
author_facet Castagnetti, Carolina
Rossi, Eduardo
Trapani, Lorenzo
author_sort Castagnetti, Carolina
building Nottingham Research Data Repository
collection Online Access
description This paper develops an estimation and testing framework for a stationary large panel model with observable regressors and unobservable common factors. We allow for slope heterogeneity and for correlation between the common factors and the regressors. We propose a two stage estimation procedure for the unobservable common factors and their loadings, based on Common Correlated Effects estimator and the Principal Component estimator. We also develop two tests for the null of no factor structure: one for the null that loadings are cross sectionally homogeneous, and one for the null that common factors are homogeneous over time. Our tests are based on using extremes of the estimated loadings and common factors. The test statistics have an asymptotic Gumbel distribution under the null, and have power versus alternatives where only one loading or common factor differs from the others. Monte Carlo evidence shows that the tests have the correct size and good power.
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publishDate 2015
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spelling nottingham-469432020-05-04T20:10:14Z https://eprints.nottingham.ac.uk/46943/ Inference on factor structures in heterogeneous panels Castagnetti, Carolina Rossi, Eduardo Trapani, Lorenzo This paper develops an estimation and testing framework for a stationary large panel model with observable regressors and unobservable common factors. We allow for slope heterogeneity and for correlation between the common factors and the regressors. We propose a two stage estimation procedure for the unobservable common factors and their loadings, based on Common Correlated Effects estimator and the Principal Component estimator. We also develop two tests for the null of no factor structure: one for the null that loadings are cross sectionally homogeneous, and one for the null that common factors are homogeneous over time. Our tests are based on using extremes of the estimated loadings and common factors. The test statistics have an asymptotic Gumbel distribution under the null, and have power versus alternatives where only one loading or common factor differs from the others. Monte Carlo evidence shows that the tests have the correct size and good power. Elsevier 2015-01 Article PeerReviewed Castagnetti, Carolina, Rossi, Eduardo and Trapani, Lorenzo (2015) Inference on factor structures in heterogeneous panels. Journal of Econometrics, 184 (1). pp. 145-157. ISSN 0304-4076 Large panels; CCE estimator; Principal Component estimator; Testing for factor structure; Extreme Value distribution http://www.sciencedirect.com/science/article/pii/S0304407614001808 10.1016/j.jeconom.2014.08.004 10.1016/j.jeconom.2014.08.004 10.1016/j.jeconom.2014.08.004
spellingShingle Large panels; CCE estimator; Principal Component estimator; Testing for factor structure; Extreme Value distribution
Castagnetti, Carolina
Rossi, Eduardo
Trapani, Lorenzo
Inference on factor structures in heterogeneous panels
title Inference on factor structures in heterogeneous panels
title_full Inference on factor structures in heterogeneous panels
title_fullStr Inference on factor structures in heterogeneous panels
title_full_unstemmed Inference on factor structures in heterogeneous panels
title_short Inference on factor structures in heterogeneous panels
title_sort inference on factor structures in heterogeneous panels
topic Large panels; CCE estimator; Principal Component estimator; Testing for factor structure; Extreme Value distribution
url https://eprints.nottingham.ac.uk/46943/
https://eprints.nottingham.ac.uk/46943/
https://eprints.nottingham.ac.uk/46943/