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The KOF Economic Barometer, Version 2014

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Abstract

This paper presents a composite leading indicator for the Swiss business cycle corresponding to the growth rate cycle concept. It is the result of a complete overhaul of the KOF Economic Barometer that has been published by the KOF Swiss Economic Institute on a monthly basis since 1976. In line with this tradition, the calculation of the new KOF Barometer comprises two main stages. The first consists of the variable selection procedure; and in the second stage these variables are subsequently transformed into one leading indicator. Whereas in the previous versions of the KOF Barometer six to 25 variables survived the first stage, the new - less discretionary and more automated - version of the first stage is much more generous. Currently, out of a set of 476 variables resulting in 4356 transformations thereof that are tested in the first stage, 219 variables manage to enter the second stage. The increased number of variables underlying the second stage allows a relatively stable and robust KOF Barometer - compared to its previous versions - that has hence no longer to rely on filtering techniques to reduce the noise in the final indicator. In a (pseudo-) real-time analysis the characteristics of the new KOF Barometer are compared to the previous versions and other alternatives.

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  • Klaus Abberger & Michael Graff & Boriss Siliverstovs & Jan-Egbert Sturm, 2014. "The KOF Economic Barometer, Version 2014," KOF Working papers 14-353, KOF Swiss Economic Institute, ETH Zurich.
    • Handle: RePEc:kof:wpskof:14-353
    DOI: 10.3929/ethz-a-010102658
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    as
    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Klaus Abberger & Wolfgang Nierhaus, 2009. "Months for cyclical dominance and the Ifo Business Climate," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(07), pages 11-19, April.
    3. Filippo Altissimo & Antonio Bassanetti & Riccardo Cristadoro & Lucrezia Reichlin & Giovanni Veronese, 2001. "The construction of coincident and leading indicators for the euro area business cycler of the euro area business cycle," Temi di discussione (Economic working papers) 434, Bank of Italy, Economic Research and International Relations Area.
    4. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    5. Adrian Pagan & Don Harding, 2005. "A suggested framework for classifying the modes of cycle research," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 151-159.
    6. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    7. Schwert, G William, 2002. "Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 5-17, January.
    8. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    9. 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.
    10. Lippi, Marco & Reichlin, Lucrezia & Hallin, Marc & Forni, Mario & Altissimo, Filippo & Cristadoro, Riccardo & Veronese, Giovanni & Bassanetti, Antonio, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers.
    11. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
    12. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    13. Jacobs, Jan P.A.M. & Otter, Pieter W. & den Reijer, Ard H.J., 2012. "Information, data dimension and factor structure," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 80-91.
    14. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, March.
    15. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    16. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    17. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    18. Boriss Siliverstovs, 2011. "The Real-Time Predictive Content of the KOF Economic Barometer," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 147(III), pages 353-375, September.
    19. Proietti, Tommaso, 2005. "New algorithms for dating the business cycle," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 477-498, April.
    20. Siliverstovs Boriss & Kholodilin Konstantin A., 2012. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 429-444, August.
    21. Filippo Altissimo & Antonio Bassanetti & Riccardo Cristadoro & Mario Forni & Marco Lippi & Lucrezia Reichlin & Giovanni Veronese, 2001. "A real time coincident indicator of the euro area business cycle," Temi di discussione (Economic working papers) 436, Bank of Italy, Economic Research and International Relations Area.
    22. Baoline Chen, 2007. "An Empirical Comparison of Methods for Temporal Distribution and Interpolation at the National Accounts," BEA Papers 0077, Bureau of Economic Analysis.
    23. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    24. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    25. 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.
    26. 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.
    27. Boriss Siliverstovs, 2012. "Keeping a Finger on the Pulse of the Economy," KOF Working papers 12-302, KOF Swiss Economic Institute, ETH Zurich.
    28. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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    1. 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.
    2. 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.
    3. Erhan Uluceviz & Kamil Yilmaz, 2020. "Real-financial connectedness in the Swiss economy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-20, December.
    4. Vera Z. Eichenauer & Ronald Indergand & Isabel Z. Martínez & Christoph Sax, 2022. "Obtaining consistent time series from Google Trends," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 694-705, April.
    5. Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2019. "Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland," International Journal of Central Banking, International Journal of Central Banking, vol. 15(2), pages 151-178, June.
    6. Boriss Siliverstovs, 2015. "Dissecting the purchasing managers' index," KOF Working papers 15-376, KOF Swiss Economic Institute, ETH Zurich.
    7. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    8. Andreas Brunhart, 2019. "Der neue Konjunkturindex „KonSens“: Ein gleichlaufender, vierteljährlicher Sammelindikator für Liechtenstein," Arbeitspapiere 62, Liechtenstein-Institut.
    9. Michael Graff & Dominik Studer, 2018. "Konstruktion von Sammelindikatoren für den Konjunkturverlauf bei prekärer Datenlage am Beispiel Montenegros," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 12(3), pages 81-91, October.
    10. Peter Kugler & George Sheldon, 2023. "A monthly leading indicator of Swiss GDP growth based on Okun’s law," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-14, December.
    11. Marc Burri, 2023. "Do daily lead texts help nowcasting GDP growth?," IRENE Working Papers 23-02, IRENE Institute of Economic Research.
    12. 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.

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    Keywords

    Business cycles; Growth rate cycles; Composite indicators; Leading indicators; Principal component analysis; Real-time simulations;
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