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Using rule-based updating procedures to improve the performance of composite indicators

Author

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  • Abberger, Klaus
  • Graff, Michael
  • Siliverstovs, Boriss
  • Sturm, Jan-Egbert

Abstract

Ideally, the set of variables underlying composite indicators is checked and updated when needed on a regular basis. In practise, the timing and procedures of these updates are usually chosen ad hoc. We suggest a rule-based indicator selection updating procedure, performed at regular intervals, which reduces the arbitrariness of this process. We apply this procedure to one of the most prominent targeted composite leading indicator for Switzerland, which is based on bivariate associations of potential variables with a reference series reflecting the Swiss growth rate cycle. We show that in a simulated real-time analysis the targeted indicator selection procedure outperforms the widely used approach to combine as many potential variables as possible. Furthermore, the regular updating procedure preserves the leading properties of the composite indicator with respect to the reference time series, as compared to the same composite indicator without such updates.

Suggested Citation

  • Abberger, Klaus & Graff, Michael & Siliverstovs, Boriss & Sturm, Jan-Egbert, 2018. "Using rule-based updating procedures to improve the performance of composite indicators," Economic Modelling, Elsevier, vol. 68(C), pages 127-144.
  • Handle: RePEc:eee:ecmode:v:68:y:2018:i:c:p:127-144
    DOI: 10.1016/j.econmod.2017.06.014
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    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    2. 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.
    3. 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.
    4. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1113-1141, December.
    5. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    6. 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.
    7. Hendry, David F., 1997. "On congruent econometric relations : A comment," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 47(1), pages 163-190, December.
    8. Rua, Antonio & Nunes, Luis C., 2005. "Coincident and leading indicators for the euro area: A frequency band approach," International Journal of Forecasting, Elsevier, vol. 21(3), pages 503-523.
    9. Baoline Chen, 2007. "An Empirical Comparison of Methods for Temporal Distribution and Interpolation at the National Accounts," BEA Papers 0077, Bureau of Economic Analysis.
    10. 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.
    11. 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.
    12. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    13. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    14. Luciani, Matteo, 2014. "Forecasting with approximate dynamic factor models: The role of non-pervasive shocks," International Journal of Forecasting, Elsevier, vol. 30(1), pages 20-29.
    15. 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.
    16. Altissimo, Filippo & Bassanetti, Antonio & Cristadoro, Riccardo & Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia & Veronese, Giovanni, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers.
    17. 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.
    18. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    19. 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.
    20. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    21. Michael Graff, 2010. "Does a multi-sectoral design improve indicator-based forecasts of the GDP growth rate? Evidence from Switzerland," Applied Economics, Taylor & Francis Journals, vol. 42(21), pages 2759-2781.
    22. 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.
    23. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    24. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    25. Gabe J. Bondt & Elke Hahn, 2014. "Introducing the Euro Area‐wide Leading Indicator (ALI): Real‐Time Signals of Turning Points in the Growth Cycle from 2007 to 2011," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 47-68, January.
    26. 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.
    27. Bair, Eric & Hastie, Trevor & Paul, Debashis & Tibshirani, Robert, 2006. "Prediction by Supervised Principal Components," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 119-137, March.
    28. Giannone, Domenico & Reichlin, Lucrezia & 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.
    29. 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.
    30. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu Juan, 2013. "Predicting Recessions with Factor Linear Dynamic Harmonic Regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 481-499, September.
    31. 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.
    32. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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    Cited by:

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    2. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2018. "σ-µ efficiency analysis: A new methodology for evaluating units through composite indices," MPRA Paper 83569, University Library of Munich, Germany.
    3. Emilian DOBRESCU, 2020. "Self-fulfillment degree of economic expectations within an integrated space: The European Union case study," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-32, December.
    4. Andrea De Montis & Vittorio Serra & Amedeo Ganciu & Antonio Ledda, 2020. "Assessing Landscape Fragmentation: A Composite Indicator," Sustainability, MDPI, Open Access Journal, vol. 12(22), pages 1-23, November.
    5. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    6. Brunhart, Andreas, 2019. "Der neue Konjunkturindex "KonSens": Ein gleichlaufender, vierteljährlicher Sammelindikator für Liechtenstein," EconStor Preprints 225261, ZBW - Leibniz Information Centre for Economics.

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    More about this item

    Keywords

    Composite leading indicators; Indicator selection; Real-time analyses;
    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

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