By the same authors

From the same journal

A Nonlinear Panel Data Model of Cross-sectional Dependence

Research output: Contribution to journalArticlepeer-review

Published copy (DOI)



Publication details

JournalJournal of Econometrics
DateAccepted/In press - 5 Jan 2014
DateE-pub ahead of print - 17 Jan 2014
DatePublished (current) - Apr 2014
Issue number2
Number of pages24
Pages (from-to)134-157
Early online date17/01/14
Original languageEnglish


This paper proposes a nonlinear panel data model which can endogenously
generate both 'weak' and 'strong' cross-sectional dependence. The model's
distinguishing characteristic is that a given agent's behaviour is influenced
by an aggregation of the views or actions of those around them. The model
allows for considerable flexibility in terms of the genesis of this herding or
clustering type behaviour. At an econometric level, the model is shown to nest
various extant dynamic panel data models. These include panel AR models,
spatial models, which accommodate weak dependence only, and panel models where cross-sectional averages or factors exogenously generate strong, but not weak, cross sectional dependence. An important implication is that the appropriate model for the aggregate series becomes intrinsically nonlinear, due to the clustering behaviour, and thus requires the disaggregates to be simultaneously considered with the aggregate. We provide the associated asymptotic theory for estimation and inference. This is supplemented with Monte Carlo studies and two empirical applications which indicate the utility of our proposed model as a vehicle to model different types of cross-sectional dependence.

    Research areas

  • Nonlinear Panel Data Model, Clustering, Cross-section dependence, Factor Models, Monte Carlo Simulations, Application to Stock Returns and Ination Expectations

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