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Dissecting the purchasing managers' index

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  • Boriss Siliverstovs

Abstract

We apply the novel approach of Siliverstovs (2015) to modelling data sampled at different frequencies in order to scrutinise the composition of one of the most influential economic indicators in Switzerland. The Purchasing Managers' Index consists of eight sub-indices out of which only five enter the total index with differentiated weights, which were specified for its American counterpart about thirty years ago. In this paper, we address the question whether the current fixed weighting scheme of the PMI components is supported by the data. We find that the relative weights of the PMI components are generally supported by the data, except the fact that one component, found very informative for explaining GDP growth, is currently omitted from the PMI composition.

Suggested Citation

  • Boriss Siliverstovs, 2015. "Dissecting the purchasing managers' index," KOF Working papers 15-376, KOF Swiss Economic Institute, ETH Zurich.
  • Handle: RePEc:kof:wpskof:15-376
    DOI: 10.3929/ethz-a-010402982
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    References listed on IDEAS

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    1. 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.
    2. Kajal Lahiri & Wenxiong Yao, 2012. "Should transportation output be included as part of the coincident indicators system?," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2012(1), pages 1-24.
    3. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    4. 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.
    5. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    6. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    7. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    8. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    9. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    10. Gad Levanon & Ataman Ozyildirim & Brian Schaitkin & Justyna Zabinska, 2011. "Comprehensive Benchmark Revisions for The Conference Board Leading Economic Index® for the United States," Economics Program Working Papers 11-06, The Conference Board, Economics Program.
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    Keywords

    GDP growth; MIDAS; LASSO; MIDASSO; PMI; Real-time data; Switzerland;
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