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Abstract:
In empirical practice dynamic factor models are typically estimated by performing a Principal Component Analysis (PCA) on the static factor representation. A two-step PC estimator and a sequential least-squares approach is proposed to estimate the original dynamic factors that enter the model with a prespecified number of lags. The identification of the dynamic factors subject to the usual normalization restrictions is analyzed and the consistency of the estimator is established. Monte Carlo simulations suggest that the sequential least-squares estimator outperforms the two-step PCA approach.
