Handling Distinct Correlated Effects with CCE

Stauskas, Ovidijus and De Vos, Ignace (2024): Handling Distinct Correlated Effects with CCE.

Abstract

The Common Correlated Effects (CCE) approach by Pesaran2006 is a popular method for estimating panel data models with interactive effects. Due to its simplicity, i.e. unobserved common factors are approximated with cross-section averages of the observables, the estimator is highly flexible and lends itself to a wide range of applications. Despite such flexibility, however, properties of CCE estimators are typically only examined under the restrictive assumption that all the observed variables load on the same set of factors, which ensures joint identification of the factor space. In this paper, we take a different perspective, and explore the empirically relevant case where the dependent and explanatory variables are driven by distinct but correlated factors. Hence, we consider the case of \emph{Distinct Correlated Effects}. Such settings can be argued to be relevant for practice, for instance in studies linking economic growth to climatic variables. In so doing, we consider panel dimensions such that T/N ratio is finite asymptotically, which is known to induce an asymptotic bias for the pooled CCE estimator even under the usual common factor assumption. We subsequently develop a robust boostrap-based toolbox that enables asymptotically valid inference in both homogeneous and heterogeneous panels, without requiring knowledge about whether factors are distinct or common.

• Item Type: MPRA Paper
• Language: English
• Keywords: Panel data, bootstrap, interactive effects, CCE, factors, information criterion
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models

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