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Weekly Economic Activity: Measurement and Informational Content

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  • Philipp Wegmüller
  • Christian Glocker

    (WIFO)

  • Valentino Guggia

Abstract

We construct a composite index to measure real activity of the Swiss economy on a weekly frequency. The index is based on a novel high-frequency data-set capturing economic activity across distinct dimensions over a long-time horizon. An adequate adjustment of raw data prior to deriving the latent factor is crucial for obtaining precise business cycle signals. By means of a real-time evaluation, we highlight the importance of our proposed adjustment procedure: first, our weekly index significantly outperforms a comparable index without adjusted input variables; secondly, the weekly index outperforms established monthly indicators in nowcasting GDP growth. These insights should help improve recently developed high-frequency indicators.

Suggested Citation

  • Philipp Wegmüller & Christian Glocker & Valentino Guggia, 2021. "Weekly Economic Activity: Measurement and Informational Content," WIFO Working Papers 627, WIFO.
  • Handle: RePEc:wfo:wpaper:y:2021:i:627
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    File URL: https://www.wifo.ac.at/wwa/pubid/67070
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    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. Siem Jan Koopman & John A. D. Aston, 2006. "A non-Gaussian generalization of the Airline model for robust seasonal adjustment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(5), pages 325-349.
    3. Paulo Rodrigues & Paulo Esteves, 2010. "Calendar effects in daily ATM withdrawals," Economics Bulletin, AccessEcon, vol. 30(4), pages 2587-2597.
    4. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    5. Christian Glocker & Philipp Wegmueller, 2020. "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, vol. 58(1), pages 73-105, January.
    6. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    7. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May.
    8. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346, April.
    9. Michael Graff & Klaus Abberger & Boriss Siliverstovs & Jan-Egbert Sturm, 2014. "Das neue KOF Konjunkturbarometer – Version 2014," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 8(1), pages 91-106, March.
    10. Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020. "A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth," International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.
    11. Dalla, Violetta & Giraitis, Liudas & Phillips, Peter C. B., 2022. "Robust Tests For White Noise And Cross-Correlation," Econometric Theory, Cambridge University Press, vol. 38(5), pages 913-941, October.
    12. Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2018. "Markov-switching dynamic factor models in real time," International Journal of Forecasting, Elsevier, vol. 34(4), pages 598-611.
    13. 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.
    14. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    15. Gerhard Fenz & Helmut Stix, 2021. "Monitoring the economy in real time with the weekly OeNB GDP indicator: background, experience and outlook," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/20-Q1/, pages 17-40.
    16. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
    17. Marc Burri & Daniel Kaufmann, 2020. "A daily fever curve for the Swiss economy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-11, December.
    18. Ollech, Daniel, 2018. "Seasonal adjustment of daily time series," Discussion Papers 41/2018, Deutsche Bundesbank.
    19. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    20. 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.
    21. Konstantin Büchel & Stefan Legge & Vincent Pochon & Philipp Wegmüller, 2020. "Swiss trade during the COVID-19 pandemic: an early appraisal," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-15, December.
    22. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    23. 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.
    24. Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
    25. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    26. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-368, July.
    27. Florian Eckert & Philipp Kronenberg & Heiner Mikosch & Stefan Neuwirth, 2020. "Tracking Economic Activity With Alternative High-Frequency Data," KOF Working papers 20-488, KOF Swiss Economic Institute, ETH Zurich.
    28. 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.
    29. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    30. Jordan, Thomas J., 2016. "The impact of international spillovers on Swiss inflation and the exchange rate," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 262-265.
    31. Eraslan, Sercan & Götz, Thomas, 2021. "An unconventional weekly economic activity index for Germany," Economics Letters, Elsevier, vol. 204(C).
    32. 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.
    33. Camacho, Maximo & Dal Bianco, Marcos & Martinez-Martin, Jaime, 2015. "Toward a more reliable picture of the economic activity: An application to Argentina," Economics Letters, Elsevier, vol. 132(C), pages 129-132.
    34. Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.
    35. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    36. 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.
    37. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
    38. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    39. António Rua & Nuno Lourenço, 2020. "The DEI: tracking economic activity daily during the lockdown," Working Papers w202013, Banco de Portugal, Economics and Research Department.
    40. Rosen, Dan & Saunders, David, 2010. "Risk factor contributions in portfolio credit risk models," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 336-349, February.
    41. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    42. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
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    2. Martin Brown & Matthias R. Fengler & Jonas Huwyler & Winfried Koeniger & Rafael Lalive & Robert Rohrkemper, 2023. "Monitoring consumption Switzerland: data, background, and use cases," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-16, December.
    3. Marc Burri, 2023. "Do daily lead texts help nowcasting GDP growth?," IRENE Working Papers 23-02, IRENE Institute of Economic Research.
    4. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas, 2023. "Nowcasting Economic Activity Using Electricity Market Data: The Case of Lithuania," Economies, MDPI, vol. 11(5), pages 1-21, May.
    5. Laura Felber & Dr. Simon Beyeler, 2023. "Nowcasting economic activity using transaction payments data," Working Papers 2023-01, Swiss National Bank.
    6. Sylvia Kaufmann, 2022. "Covid-19 outbreak and beyond: A retrospect on the information content of registered short-time workers for GDP now- and forecasting," Working Papers 22.02, Swiss National Bank, Study Center Gerzensee.
    7. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    8. Yang, Jinyu & Dong, Dayong & Liang, Chao, 2024. "Climate policy uncertainty and the U.S. economic cycle," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    9. Sylvia Kaufmann, 2023. "Covid-19 outbreak and beyond: a retrospect on the information content of short-time workers for GDP now- and forecasting," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-10, December.
    10. Philipp Wegmüller & Christian Glocker, 2023. "US weekly economic index: Replication and extension," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 977-985, September.

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    Keywords

    Business cycle index; Dynamic factor model; High-frequency data; Nowcasting;
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