 Assessment of Monthly GDP Growth Using Temporal Disaggregation MethodsRussian Journal of Money and Finance, 2022, vol. 81, issue 2, 79-104 Abstract: This paper presents an approach to the estimation of monthly GDP growth in Russia using temporal disaggregation. This method represents a balanced view of the current economic situation that relies on a model framework and encompasses all economic sectors. The paper makes a meaningful contribution to the existing academic literature on the topic by combining a description of the most advanced disaggregation methods with their practical application on Russian data. From the methods used in 16 model varieties considered for the 2004–2020 period, we select those which are most robust to data revision, and which are most suitable for nowcasting purposes. Averaging the results from these methods improves the robustness of the estimates and increases practical flexibility for macroeconomists. The resulting indicator includes the most recent and comprehensive information about economic activity, which is essential in the event of unexpected economic developments. The findings of this research may be useful for assessment of the current economic situation for verifying GDP forecasts and for the development of monetary policy in Russia. Keywords: economic activity; temporal disaggregation; Chow–Lin; GDP; nowcast; forecast (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bkr:journl:v:81:y:2022:i:2:p:79-104 Access Statistics for this article Russian Journal of Money and Finance is currently edited by Ksenia Yudaeva More articles in Russian Journal of Money and Finance from Bank of Russia Contact information at EDIRC. |
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