What is it about?

The Common Correlated Effects methodology is a highly popular estimation approach to estimate panel data models with unobserved common factors. Essentially, by adding cross-section averages (CA) of the observables to the regression model, the method controls for a wide variety of possible unobserved heterogeneity in the dataset. The method is simple and effective, but it was not originally intended for use in dynamic panel data models. Dynamic models include lags of the dependent variable as regressors to account for the slow adaptation of economic variables to changes in their determinants, and are hence very common in empirical economics. In this paper we show how the Common Correlated Effects (CCE) estimator should be specified in dynamic models. That is, we illustrate which cross-section averages should be added to the specification to capture the unobserved heterogeneity. Next, we show that although adding the appropriate CA to the specification will control for the unobserved heterogeneity, which is essential for consistency, an important by-product of this augmentation is that it leads to substantial bias in dynamic models estimated using datasets with a finite time series dimension. In response, we introduce a simple bias correction which, as shown in both theory and simulations, effectively removes all bias even in finite samples. The resulting bias-adjusted CCE estimator is a highly effective, unbiased estimator and provides a reliable tool for inference in the dynamic model.

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Why is it important?

Given that economic variables tend to be highly persistent and react slowly to changes in their determinants, dynamic panel (longitudinal) data models are very common in empirical economics. The CCE methodology is becoming a workhorse in empirical work, yet when applied to estimate dynamic specifications we show that the methodology is unreliable for inference even when both the time series and cross-section dimension of the panel dataset tend to infinity. This implies that inference based on the estimated coefficients will always lead to misleading conclusions. In response, we develop a corrected CCE estimator and show, through both theory and simulations, that it will allow reliable estimation and inference even in small datasets.


The CCE methodology is commonly applied to estimate panel data models with unobserved common factors. However, given the widespread use of dynamic specifications in empirical economics it was key that the methodology, initially developed for static specifications, is also appropriately extended to the dynamic setting, and its strengths and weaknesses are exposed. In so doing, we found that the original methodology, while very simple and powerful for approximating the unobserved common factors, has the important downside of being unreliable for inference due to bias, even as both dimensions of the panel dataset grow large. The key contribution of this article is that we managed to resolve this problem with a simple correction, and that the resulting corrected estimator displays the same favourable finite sample properties as the CCE estimators display in the original static model.

Dr. Ignace De Vos
Lunds Universitet

Read the Original

This page is a summary of: Bias-Corrected Common Correlated Effects Pooled Estimation in Dynamic Panels, Journal of Business and Economic Statistics, September 2019, Taylor & Francis,
DOI: 10.1080/07350015.2019.1654879.
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