In the context of Dynamic Factor Models (DFM), we compare point and interval estimates of
the underlying unobserved factors extracted using small and big-data procedures. Our paper
differs from previous works in the related literature in several ways. First, we focus on factor
extraction rather than on prediction of a given variable in the system. Second, the comparisons
are carried out by implementing the procedures considered to the same data. Third, we are
interested not only on point estimates but also on confidence intervals for the factors. Based on
a simulated system and the macroeconomic data set popularized by Stock and Watson (2012),
we show that, for a given procedure, factor estimates based on different cross-sectional
dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the
Maximum Likelihood Kalman filter and smoother (KFS) factor estimates are highly correlated
with those obtained using hybrid Principal Components (PC) and KFS procedures. The PC
estimates are somehow less correlated. Finally, the PC intervals based on asymptotic
approximations are unrealistically tiny