Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model
Alexey Porshakov (),
Alexey Ponomarenko and
No 19/2015, BOFIT Discussion Papers from Bank of Finland, Institute for Economies in Transition
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 information 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.
JEL-codes: C53 C82 E17 (search for similar items in EconPapers)
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Published in Published in Zhournal Novoi Ekonomicheskoi Associacii, Volume 2, Issue 30, 2016: 60-76
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Journal Article: Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model (2016)
Working Paper: Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:bof:bofitp:2015_019
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