Series: Working Papers. 1235.
Author: Maximo Camacho, Yuliya Lovcha and Gabriel Perez-Quiros.
Published in: Studies in Nonlinear Dynamics & Econometrics, Volume 19, Issue 3 (September, 2014)
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Abstract
We examine the short-term performance of two alternative approaches to forecasting using dynamic factor models. The first approach extracts the seasonal component of the individual indicators before estimating the dynamic factor model, while the alternative uses the nonseasonally adjusted data in a model that endogenously accounts for seasonal adjustment. Our Monte Carlo analysis reveals that the performance of the former is always comparable to or even better than that of the latter in all the simulated scenarios. Our results have important implications for the factor models literature because they show that the common practice of using seasonally adjusted data in this type of model is very accurate in terms of forecasting ability. Drawing on five coincident indicators, we illustrate this result for US data.