Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model

Abstract

Real-time assessment of quarterly GDP growth rates is crucial for evaluating an economy’s current prospects given that the relevant data are normally subject to substantial delays in publication by the national statistical agencies. Large information sets of real-time indicators which could be used to approximate GDP growth rates in the quarter of interest are characterized by unbalanced data, mixed frequencies, systematic data revisions, as well as a more general curse of dimensionality problem. The latter issues could, however, be practically resolved by means of dynamic factor modeling, which has recently been recognized as a useful tool to evaluate current economic conditions by means of higher frequency indicators. Our main results show that the performance of dynamic factor models in predicting Russian GDP dynamics appears to be superior to other common alternative specifications. At the same time, we empirically show that the arrival of new data seems to consistently improve DFM’s predictive accuracy throughout sequential nowcast vintages. We also introduce an analysis of nowcast evolution resulting from the gradual expansion of the dataset of explanatory variables, as well as the framework for estimating contributions of different blocks of predictors into nowcasts of Russian GDP.

Keywords: GDP nowcast, dynamic factor models, principal components, Kalman filter, nowcast evolution

JEL Classification: C53, C82, E17