BIAS correction for dynamic factor models

In this paper we work with multivariate time series that follow a Dynamic Factor Model. In particular, we consider the setting where factors are dominated by highly persistent AutoRegressive (AR) processes, and samples that are rather small. Therefore, the factors' AR models are estimated using small sample bias correction techniques. A Monte Carlo study reveals that bias-correcting the AR coefficients of the factors allows to obtain better results in terms of prediction interval coverage. As expected, the simulation reveals that bias-correction is more successful for smaller samples. Results are gathered assuming the AR order and number of factors are known as well as unknown. We also study the advantages of this technique for a set of Industrial Production Indexes of several European countries.A.M. Alonso acknowledges support of the Spanish Ministry of Economy and Competitiveness, research projects ECO2012-38442, and ECO2015-66593. Carolina García-Martos acknowledges financial support from project DPI2011-23500, Spanish Ministry of Economy and Competitiveness