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date: 04 April 2020

Abstract and Keywords

Unbalanced panel data are common in empirical research. This chapter provides two types of estimators for panel data models in the presence of interactive effects and missing observations. One deals with the case when the common factors are deterministic and smooth in the time domain, and the proposed estimator is based on an iterative functional principal components analysis. The other method integrates the EM algorithm and the traditional principal components method, which effectively estimates the model when the factors are non-smooth and probably stochastic. Both static and dynamic panel models are considered. Extensive simulations are carried out. It is found that the EM-based methods are in general consistent whether the factors are smooth or not for both static and dynamic models.

Keywords: Keywords: missing observations, functional PCA, EM algorithm, principal components

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