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Forecasting with Leading Indicators by means of the Principal Covariate Index

Author

Listed:
  • Christiaan Heij
  • Dick van Dijk
  • Patrick J.F. Groenen

Abstract

A new method of leading index construction is proposed, which explicitly takes into account the purpose of using the index for forecasting a coincident economic indicator. This so-called principal covariate index combines the need for compressing the information in a large number of individual leading indicator variables with the objective of forecasting. In an empirical application to forecast future growth rates of the Conference Board’s Composite Coincident Index and its constituents, the forecasts of the principal covariate index are more accurate than those obtained either from the Composite Leading Index of the Conference Board or from an alternative index-based on principal components. JEL Classification: C32, C53, E27 Keywords: index construction, business cycles, principal component, principal covariate, time series forecasting, variable selection

Suggested Citation

  • Christiaan Heij & Dick van Dijk & Patrick J.F. Groenen, 2011. "Forecasting with Leading Indicators by means of the Principal Covariate Index," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2011(1), pages 73-92.
    • Handle: RePEc:oec:stdkab:5kgdwlpzs79v
    DOI: 10.1787/jbcma-2011-5kgdwlpzs79v
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    Cited by:

    1. Burcu Gurcihan Yunculer & Gonul Sengul & Arzu Yavuz, 2014. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 23-45.

    More about this item

    Keywords

    index construction; business cycles; principal component; principal covariate; time series forecasting; variable selection;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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