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Nowcasting BRIC+M in real time

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  • Dahlhaus, Tatjana
  • Guénette, Justin-Damien
  • Vasishtha, Garima

Abstract

Given the growing importance of emerging market economies (EMEs) in driving global GDP growth, timely and accurate assessments of current and future economic activity in EMEs are important for policy-makers not only in these countries, but also in advanced economies. This paper uses state-of-the-art dynamic factor models (DFMs) to nowcast real GDP growth for Brazil, Russia, India, China, and Mexico (“BRIC+M”). The DFM framework is particularly suitable for EMEs, as it enables the efficient handling of data series that are characterized by different publication lags, frequencies, and sample lengths. It also allows the extraction of model-based “news” from a data release and the assessment of the impact of such “news” on nowcast revisions. Overall, we find that the DFMs generally display a good directional accuracy and provide reliable nowcasts for GDP growth. Furthermore, the “news” pertaining to domestic indicators is the main driver of changes in nowcast revisions, while exogenous variables play a relatively minor role.

Suggested Citation

  • Dahlhaus, Tatjana & Guénette, Justin-Damien & Vasishtha, Garima, 2017. "Nowcasting BRIC+M in real time," International Journal of Forecasting, Elsevier, vol. 33(4), pages 915-935.
  • Handle: RePEc:eee:intfor:v:33:y:2017:i:4:p:915-935
    DOI: 10.1016/j.ijforecast.2017.05.002
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    9. Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
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    12. Daniela Bragoli & Jack Fosten, 2018. "Nowcasting Indian GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(2), pages 259-282, April.
    13. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    14. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023. "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers 1385, Board of Governors of the Federal Reserve System (U.S.).
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    17. Caruso, Alberto, 2018. "Nowcasting with the help of foreign indicators: The case of Mexico," Economic Modelling, Elsevier, vol. 69(C), pages 160-168.
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    21. Pérez-Quirós, Gabriel & Leiva-León, Danilo & Rots, Eyno, 2020. "Real-Time Weakness of the Global Economy: A First Assessment of the Coronavirus Crisis," CEPR Discussion Papers 14484, C.E.P.R. Discussion Papers.
    22. Andrey Zubarev & Daniil Lomonosov & Konstantin Rybak, 2022. "Estimation of the Impact of Global Shocks on the Russian Economy and GDP Nowcasting Using a Factor Model," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 49-78, June.
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    More about this item

    Keywords

    Dynamic factor model; Nowcasting; Real-time data; Emerging markets;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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