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Common correlated effect cross-sectional dependence corrections for non-linear conditional mean panel models

Sinem Hacioglu Hoke () and George Kapetanios ()
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George Kapetanios: Kings College London, Postal: Kings College London

No 683, Bank of England working papers from Bank of England

Abstract: This paper provides an approach to estimation and inference for non-linear conditional mean panel data models, in the presence of cross-sectional dependence. We modify the common correlated effects (CCE) correction of Pesaran (2006) to filter out the interactive unobserved multifactor structure. The estimation can be carried out using non-linear least squares, by augmenting the set of explanatory variables with cross-sectional averages of both linear and non-linear terms. We propose pooled and mean group estimators, derive their asymptotic distributions, and show the consistency and asymptotic normality of the coefficients of the model. The features of the proposed estimators are investigated through extensive Monte Carlo experiments. We apply our method to estimate UK banks’ wholesale funding costs and explore the non-linear relationship between public debt and output growth.

Keywords: Non-linear panel data model; cross-sectional dependence; common correlated effects estimator (search for similar items in EconPapers)
JEL-codes: C31 C33 C51 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2017-10-16
New Economics Papers: this item is included in nep-ecm
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Journal Article: Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models (2021) Downloads
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