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Nowcasting GDP growth in Russia with an incomplete dataset: A factor model approach

Nurdaulet Abilov () and Aizhan Bolatbayeva ()
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Aizhan Bolatbayeva: NAC Analytica, Nazarbayev University

No 18, NAC Analytica Working Paper from NAC Analytica, Nazarbayev University

Abstract: In this paper, we use the modified expectation-maximization algorithm of Banbura and Modugno (2014) to estimate a factor model using an incomplete and mixed-frequency dataset for Russia. We estimate and check the forecast accuracy of factor models that differ in the number of factors, the lag structure of the factors, and the presence of autocorrelation in the idiosyncratic component. We choose the best model using the root mean squared forecast error and use the model to compute news contributions to forecast revisions of GDP growth in Russia around crisis periods. We find that the benchmark model with a medium-size dataset and four factors outperforms all other versions of the factor model, simple AR(1) and random walk models. The news contributions to GDP growth revisions around economic downturns in Russia show that the benchmark factor model is extremely good at capturing the impact of new data releases on GDP growth revisions.

Keywords: Factor model; EM-algorithm; Nowcasting; Business cycle index; Russia. (search for similar items in EconPapers)
JEL-codes: C53 C55 E32 E37 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2021-12, Revised 2022-02
New Economics Papers: this item is included in nep-cis, nep-fdg, nep-for, nep-his and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:ajx:wpaper:18

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