Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model

• Published in: BOFIT discussion papers Vol. 2015; no. 19; p. 1
• Main Authors: Porshakov, Alexey, Deryugina, Elena, Ponomarenko, Alexey, Sinyakov, Andrey
• Format: Journal Article
• Language: English
• Published: Helsinki Suomen Pankki, Siirtymatalouksien Tutkimuslaitos 01.07.2015
• Subjects: Accuracy; Calendars; Datasets; Economic activity; Economic conditions; Economic forecasting; Economic growth; Economic indicators; Forecasting; Forecasting techniques; GDP; Gross Domestic Product; Growth rate; Industrial production; Macroeconomics; Real time; Studies; Variables; Russia
• ISSN: 1456-4564; 1456-5889

Real-time assessment of quarterly GDP growth rates is crucial for evaluation of economy's current perspectives given the fact that respective data is normally subject to substantial publication delays by national statistical agencies. Large infonnation sets of real-time indicators which could be used to approximate GDP growth rates in the quarter of interest are in practice 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 that has recently been recognized as a helpful tool to evaluate current economic conditions by means of higher frequency indicators. Our major results show that the performance of dynamic factor models in predicting Russian GDP dynamics appears to be superior as compared 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 the 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 now-casts of Russian GDP.