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Financial information and macroeconomic forecasts

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  • Chen, Sophia
  • Ranciere, Romain

Abstract

We study the forecasting power of financial variables for macroeconomic variables in 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices, and house prices have considerable predictive power for macroeconomic variables at the one- to four-quarter horizons. A forecasting model that includes financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85% of our sample countries at the four-quarter horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.

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  • Chen, Sophia & Ranciere, Romain, 2019. "Financial information and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1160-1174.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:3:p:1160-1174
    DOI: 10.1016/j.ijforecast.2019.03.005
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    3. Yan Carrière-Swallow & José Marzluf, 2023. "Macrofinancial Causes of Optimism in Growth Forecasts," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 509-537, June.
    4. Olga Bespalova & Mrs. Marina V Rousset, 2019. "Macrofinancial Linkages and Growth at Risk in the Dominican Republic," IMF Working Papers 2019/246, International Monetary Fund.
    5. María Paula Bonel & Daniel J. Aromí, 2021. "Assessing GDP forecasts from autoregressive models: the impact of model complexity and training dataset," Asociación Argentina de Economía Política: Working Papers 4440, Asociación Argentina de Economía Política.
    6. Jack Fosten & Shaoni Nandi, 2023. "Nowcasting from cross‐sectionally dependent panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 898-919, September.
    7. Imran Hussain Shah & Simón Sosvilla-Rivero, 2017. "Seeking price and macroeconomic stabilisation in the euro area: The role of house prices and stock prices," IREA Working Papers 201710, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    8. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    9. Paccagnini, Alessia, 2019. "Did financial factors matter during the Great Recession?," Economics Letters, Elsevier, vol. 174(C), pages 26-30.
    10. Chatelais, Nicolas & Stalla-Bourdillon, Arthur & Chinn, Menzie D., 2023. "Forecasting real activity using cross-sectoral stock market information," Journal of International Money and Finance, Elsevier, vol. 131(C).
    11. Nicolas Chatelais & Menzie Chinn & Arthur Stalla-Bourdillon, 2022. "Macroeconomic Forecasting Using Filtered Signals from a Stock Market Cross Section," Working papers 903, Banque de France.
    12. de Bondt, Gabe J. & Hahn, Elke & Zekaite, Zivile, 2021. "ALICE: Composite leading indicators for euro area inflation cycles," International Journal of Forecasting, Elsevier, vol. 37(2), pages 687-707.
    13. Lukasz Mach & Dariusz Zmarzly & Ireneusz Dabrowski & Pawel Fracz, 2020. "Comparison on Subannual Seasonality of Building Construction in European Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 241-257.
    14. Qu, Li, 2021. "A new approach to estimating earnings forecasting models: Robust regression MM-estimation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1011-1030.
    15. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).

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