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A daily fever curve for the Swiss economy

Author

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  • Marc Burri
  • Daniel Kaufmann

Abstract

Because macroeconomic data is published with a substantial delay, assessing the health of the economy during the rapidly evolving Covid-19 crisis is challenging. We develop a fever curve for the Swiss economy using publicly available daily financial market and news data. The indicator can be computed with a delay of one day. Moreover, it is highly correlated with macroeconomic data and survey indicators of Swiss economic activity. Therefore, it provides timely and reliable warning signals if the health of the economy takes a turn for the worse.

Suggested Citation

  • Marc Burri & Daniel Kaufmann, 2020. "A daily fever curve for the Swiss economy," IRENE Working Papers 20-05, IRENE Institute of Economic Research.
    • Handle: RePEc:irn:wpaper:20-05
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    References listed on IDEAS

    as
    1. Alain Galli, 2018. "Which Indicators Matter? Analyzing the Swiss Business Cycle Using a Large-Scale Mixed-Frequency Dynamic Factor Model," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 179-218, November.
    2. Jean Boivin & Marc P. Giannoni & Ilian Mihov, 2009. "Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data," American Economic Review, American Economic Association, vol. 99(1), pages 350-384, March.
    3. Daniel J. Lewis & Karel Mertens & James H. Stock, 2020. "Monitoring Real Activity in Real Time: The Weekly Economic Index," Liberty Street Economics 20200330b, Federal Reserve Bank of New York.
    4. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    5. Daniel Kaufmann, 2020. "Wie weiter mit der Tiefzinspolitik? Szenarien und Alternativen," IRENE Policy Reports 20-01, IRENE Institute of Economic Research.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
    8. Leif Anders Thorsrud, 2020. "Words are the New Numbers: A Newsy Coincident Index of the Business Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
    9. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    10. , 2020. "News Sentiment in the Time of COVID-19," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2020(08), pages 1-05, April.
    11. Kaufmann, Daniel & Lein, Sarah M., 2013. "Sticky prices or rational inattention – What can we learn from sectoral price data?," European Economic Review, Elsevier, vol. 64(C), pages 384-394.
    12. Rebecca Stuart, 2020. "The term structure, leading indicators, and recessions: evidence from Switzerland, 1974–2017," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-17, December.
    13. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
    14. Scotti, Chiara, 2016. "Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 1-19.
    15. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    16. Klaus Neusser, 2016. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-32862-1, January.
    17. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    18. Ronald Indergand & Stefan Leist, 2014. "A Real-Time Data Set for Switzerland," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 150(IV), pages 331-352, December.
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    Cited by:

    1. Santiago E. Alvarez & Sarah M. Lein, 2020. "Tracking inflation on a daily basis," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-13, December.
    2. Florian Eckert & Heiner Mikosch, 2020. "Mobility and sales activity during the Corona crisis: daily indicators for Switzerland," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-10, December.

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    More about this item

    Keywords

    Covid-19; Leading indicator; Financial market data; News sentiment; Forecasting; Switzerland;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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