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Composite Leading Indicator for the Austrian Economy. Methodology and "Real-time" Performance

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  • Jürgen Bierbaumer-Polly

Abstract

This paper describes the methodologies used for constructing a composite leading indicator for the Austrian economy (CLI-AT). First, a selection of those monthly indicators which overall fare best in showing a "steady" leading behaviour with respect to the Austrian business cycle was performed. The analysis was carried out by means of statistical methods out of the timeseries domain as well as from the frequency domain. Thirteen series have been finally classified as leading indicators. Among them, business and consumer survey data form the most prevalent group. Second, I construct the CLI-AT based on the de-trended, normalised and weighted leading series. For the de-trending procedure I use the HP filter and the weights have been obtained by means of principal components analysis. Further, idiosyncratic elements in the CLI-AT have been removed along with checking the endpoint-bias due to the HP filter smoothing procedure. I find that the "real-time" smoothed CLI-AT does not exhibit severe phase-shifts compared to a full-sample estimate. Next, I show that the CLI-AT provides a useful instrument for assessing the current and likely future direction in the Austrian business cycle. Over the period 1988-2008, the CLI-AT indicates cyclical turns with a "steady" lead in the majority of cases. Finally, in using an out-of-sample forecasting exercise it is shown that the CLI-AT carries important business cycle information and that its inclusion in a forecasting model can increase the projection quality of the underlying reference series.

Suggested Citation

  • Jürgen Bierbaumer-Polly, 2010. "Composite Leading Indicator for the Austrian Economy. Methodology and "Real-time" Performance," WIFO Working Papers 369, WIFO.
  • Handle: RePEc:wfo:wpaper:y:2010:i:369
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    References listed on IDEAS

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    1. Sandra Bilek-Steindl & Petra Sauer & Marcus Scheiblecker, 2009. "Revision Analysis of Quarterly National Accounts in Austria," WIFO Monatsberichte (monthly reports), WIFO, vol. 82(10), pages 749-766, October.
    2. Christophe Croux & Mario Forni & Lucrezia Reichlin, 2001. "A Measure Of Comovement For Economic Variables: Theory And Empirics," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 232-241, May.
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    Cited by:

    1. Jürgen Bierbaumer-Polly, 2012. "Regional Business Cycles in the Austrian Economy," WIFO Monatsberichte (monthly reports), WIFO, vol. 85(11), pages 833-848, November.
    2. Juergen Bierbaumer-Polly, 2012. "Regional and Sectoral Business Cycles - Key Features for the Austrian economy," EcoMod2012 4074, EcoMod.
    3. Martin Schneider, 2013. "Are Recent Increases of Residential Property Prices in Vienna and Austria Justified by Fundamentals?," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 29-46.
    4. Werner Hölzl & Gerhard Schwarz, 2014. "The "WIFO-Konjunkturtest": Methodology and Forecast Characteristics of the WIFO Business Cycle Survey," WIFO Monatsberichte (monthly reports), WIFO, vol. 87(12), pages 835-850, December.
    5. Jürgen Bierbaumer-Polly & Werner Hölzl, 2016. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.

    More about this item

    Keywords

    Business cycles; turning points; cyclical analysis; leading indicators; composite indicators; HP filter; principal components; out-of-sample forecasting;

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