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Forecasting with mixed frequencies

Author

Listed:
  • Michelle T. Armesto
  • Kristie M. Engemann
  • Michael T. Owyang

Abstract

A dilemma faced by forecasters is that data are not all sampled at the same frequency. Most macroeconomic data are sampled monthly (e.g., employment) or quarterly (e.g., GDP). Most financial variables (e.g., interest rates and asset prices), on the other hand, are sampled daily or even more frequently. The challenge is how to best use available data. To that end, the authors survey some common methods for dealing with mixed-frequency data. They show that, in some cases, simply averaging the higher-frequency data produces no discernible disadvantage. In other cases, however, explicitly modeling the flow of data (e.g., using mixed data sampling as in Ghysels, Santa-Clara, and Valkanov, 2004) may be more beneficial to the forecaster, especially if the forecaster is interested in constructing intra-period forecasts.

Suggested Citation

  • Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 521-536.
  • Handle: RePEc:fip:fedlrv:y:2010:i:nov:p:521-536:n:v.92no.6
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    References listed on IDEAS

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    3. 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.
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    5. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
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    Citations

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    Cited by:

    1. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    2. Lucia Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon Potter, 2014. "Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences," Journal of Business & Economic Statistics, Taylor & Francis Journals, pages 483-500.
    3. repec:spr:jbuscr:v:12:y:2016:i:2:d:10.1007_s41549-016-0008-z is not listed on IDEAS
    4. Gustavo Adolfo HERNANDEZ DIAZ & Margarita MARÍN JARAMILLO, 2016. "Pronóstico del Consumo Privado: Usando datos de alta frecuencia para el pronóstico de variables de baja frecuencia," ARCHIVOS DE ECONOMÍA 014828, DEPARTAMENTO NACIONAL DE PLANEACIÓN.
    5. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    6. repec:eee:eneeco:v:67:y:2017:i:c:p:83-90 is not listed on IDEAS
    7. Marie Bessec & Othman Bouabdallah, 2015. "Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 360-384, June.
    8. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    9. repec:eee:ecofin:v:42:y:2017:i:c:p:421-432 is not listed on IDEAS
    10. Raul Ibarra & Luis M. Gomez-Zamudio, 2017. "Are Daily Financial Data Useful for Forecasting GDP? Evidence from Mexico," ECONOMIA JOURNAL OF THE LATIN AMERICAN AND CARIBBEAN ECONOMIC ASSOCIATION, ECONOMIA JOURNAL OF THE LATIN AMERICAN AND CARIBBEAN ECONOMIC ASSOCIATION, vol. 0(Spring 20), pages 173-203, April.
    11. repec:eee:touman:v:46:y:2015:i:c:p:454-464 is not listed on IDEAS
    12. Lee, Chien-Chiang & Chen, Mei-Ping & Chang, Chi-Hung, 2014. "Industry co-movement and cross-listing: Do home country factors matter?," Japan and the World Economy, Elsevier, vol. 32(C), pages 96-110.
    13. repec:eee:ecmode:v:66:y:2017:i:c:p:132-138 is not listed on IDEAS
    14. Kvedaras, Virmantas & Zemlys, Vaidotas, 2012. "Testing the functional constraints on parameters in regressions with variables of different frequency," Economics Letters, Elsevier, vol. 116(2), pages 250-254.
    15. repec:bla:jorssa:v:180:y:2017:i:2:p:353-407 is not listed on IDEAS
    16. Kamini Solanki & Yudhvir Seetharam, 2014. "Is consumer confidence an indicator of JSE performance?," Contemporary Economics, University of Finance and Management in Warsaw, vol. 8(3), September.
    17. Laura D´Amato & Lorena Garegnani & Emilio Blanco, 2015. "GDP Nowcasting: Assessing business cycle conditions in Argentina," BCRA Working Paper Series 201569, Central Bank of Argentina, Economic Research Department.
    18. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
    19. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    20. 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.
    21. Guay, Alain & Maurin, Alain, 2015. "Disaggregation methods based on MIDAS regression," Economic Modelling, Elsevier, vol. 50(C), pages 123-129.
    22. Trujillo-Barrera, Andres & Pennings, Joost M.E., 2013. "Energy and Food Commodity Prices Linkage: An Examination with Mixed-Frequency Data," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150465, Agricultural and Applied Economics Association.
    23. Roy Verbaan & Wilko Bolt & Carin van der Cruijsen, 2017. "Using debit card payments data for nowcasting Dutch household consumption," DNB Working Papers 571, Netherlands Central Bank, Research Department.

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    Keywords

    Economic forecasting ; Econometric models;

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