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Forecasting growth during the Great Recession: is financial volatility the missing ingredient?

Citations

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Guest Contribution: “Nowcasting Global GDP Growth”
    by Menzie Chinn in Econbrowser on 2015-03-12 09:56:18

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:

  1. Laurent Ferrara & Stéphane Lhuissier & Fabien Tripier, 2018. "Uncertainty Fluctuations: Measures, Effects and Macroeconomic Policy Challenges," Financial and Monetary Policy Studies, in: Laurent Ferrara & Ignacio Hernando & Daniela Marconi (ed.), International Macroeconomics in the Wake of the Global Financial Crisis, pages 159-181, Springer.
  2. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
  3. Laurent Ferrara & Clément Marsilli, 2019. "Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach," The World Economy, Wiley Blackwell, vol. 42(3), pages 846-875, March.
  4. Grégory Levieuge, 2017. "Explaining and forecasting bank loans. Good times and crisis," Applied Economics, Taylor & Francis Journals, vol. 49(8), pages 823-843, February.
  5. Chauvet, Marcelle & Senyuz, Zeynep & Yoldas, Emre, 2015. "What does financial volatility tell us about macroeconomic fluctuations?," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 340-360.
  6. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
  7. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
  8. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
  9. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
  10. Marie Bessec, 2019. "Revisiting the transitional dynamics of business cycle phases with mixed-frequency data," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 711-732, August.
  11. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
  12. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  13. Fatemeh Salimi Namin, 2020. "Exchange Rates, Stock Prices, and Stock Market Uncertainty," AMSE Working Papers 2037, Aix-Marseille School of Economics, France.
  14. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
  15. Cremers, Martijn & Fleckenstein, Matthias & Gandhi, Priyank, 2021. "Treasury yield implied volatility and real activity," Journal of Financial Economics, Elsevier, vol. 140(2), pages 412-435.
  16. Laine, Olli-Matti & Lindblad, Annika, 2020. "Nowcasting Finnish GDP growth using financial variables: a MIDAS approach," BoF Economics Review 4/2020, Bank of Finland.
  17. Junttila, Juha & Vataja, Juuso, 2018. "Economic policy uncertainty effects for forecasting future real economic activity," Economic Systems, Elsevier, vol. 42(4), pages 569-583.
  18. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
  19. repec:dau:papers:123456789/15246 is not listed on IDEAS
  20. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
  21. Stefan Gebauer, 2017. "The Use of Financial Market Variables in Forecasting," DIW Roundup: Politik im Fokus 115, DIW Berlin, German Institute for Economic Research.
  22. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
  23. repec:zbw:bofitp:2017_019 is not listed on IDEAS
  24. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
  25. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
  26. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland, Institute for Economies in Transition.
  27. Fatemeh Salimi, 2020. "Exchange Rates, Stock Prices, and Stock Market Uncertainty," Working Papers halshs-03007904, HAL.
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