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A supply-side GDP nowcasting model

Author

Listed:
  • Alejandro Fernández Cerezo

    (Banco de España)

Abstract

Rationale The recent shocks to the Spanish economy, linked to both COVID-19 and rising energy prices, have had an uneven impact across sectors of activity, underscoring the importance of monitoring the supply side of economic activity. Takeaways •This article presents a model for forecasting quarterly GDP using a combination of monthly indicators to estimate the growth of gross value added for each sector of activity. •The results in terms of forecasting accuracy evidence the usefulness of a sectoral approach, as a complementary tool for monitoring economic activity in the short term.

Suggested Citation

  • Alejandro Fernández Cerezo, 2023. "A supply-side GDP nowcasting model," Economic Bulletin, Banco de España, issue 2023/Q1.
    • Handle: RePEc:bde:journl:y:2023:i:01:n:18
    DOI: https://doi.org/10.53479/29778
    as

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    References listed on IDEAS

    as
    1. Luis Julián Álvarez & Alberto Cabrero & Alberto Urtasun, 2014. "A procedure for short-term GDP forecasting," Economic Bulletin, Banco de España, issue OCT, pages 29-35, October.
    2. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
    3. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
    4. Daniel Rees, 2020. "What Comes Next?," BIS Working Papers 898, Bank for International Settlements.
    5. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    6. Clements, Michael P & Galvão, Ana Beatriz, 2008. "Macroeconomic Forecasting With Mixed-Frequency Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 546-554.
    7. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    8. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    9. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    10. repec:bde:journl:v:10:y:2014:p:13 is not listed on IDEAS
    11. Maximo Camacho & Gabriel Perez Quiros, 2011. "Spain‐Sting: Spain Short‐Term Indicator Of Growth," Manchester School, University of Manchester, vol. 79(s1), pages 594-616, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Economic cycle; growth; time series; forecasts; sectors;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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