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A Probability Model of The Coincident Economic Indicators

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Author Info
James H. Stock
Mark W. Watson

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Abstract

The Index of Coincident Economic Indicators, currently compiled by the U.S. Department of Commerce, is designed to measure the state of overall economic activity. The index is constructed as a weighted average of four key macroeconomic time series, where the weights are obtained using rules that dare to the early days of business cycle analysis. This paper presents an explicit rime series model (formally, a dynamic factor analysis or "single index" model) that implicitly defines a variable that can be thought of as the overall state of the economy. Upon estimating this model using data from 1959-1987, the estimate of this unobserved variable is found to be highly correlated with the official Commerce Department series, particularly over business cycle horizons. Thus this model provides a formal rationalization for the traditional methodology used to develop the Coincident Index. Initial exploratory exercises indicate that traditional leading variables can prove useful in forecasting the short-run growth in this series.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 2772.

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Date of creation: Nov 1988
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Handle: RePEc:nbr:nberwo:2772

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Kling, John L, 1987. "Predicting the Turning Points of Business and Economic Time Series," Journal of Business, University of Chicago Press, vol. 60(2), pages 201-38, April. [Downloadable!] (restricted)
  2. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall. [Downloadable!] (restricted)
  3. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March. [Downloadable!] (restricted)
  4. Koch, Paul D & Rasche, Robert H, 1988. "An Examination of the Commerce Department Leading-Indicator Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 167-87, April.
  5. Auerbach, Alan J, 1982. "The Index of Leading Indicators: "Measurement without Theory," Thirty-Five Years Later," The Review of Economics and Statistics, MIT Press, vol. 64(4), pages 589-95, November. [Downloadable!] (restricted)
  6. F. Javier Fernandez Macho & Andrew C. Harvey & James H. Stock, 1987. "Forecasting and Interpolation Using Vector Autoregressions with Common Trends," Annales d'Economie et de Statistique, ADRES, issue 6-7, pages 12, Avril-Sep. [Downloadable!]
  7. Friedman, Benjamin M, 1988. "Monetary Policy without Quantity Variables," American Economic Review, American Economic Association, vol. 78(2), pages 440-45, May. [Downloadable!] (restricted)
  8. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March. [Downloadable!] (restricted)
  9. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis. [Downloadable!]
  10. Beatrice N. Vaccara & Victor Zarnowitz, 1978. "Forecasting with the Index of Leading Indicators," NBER Working Papers 0244, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  11. Francis X. Diebold & Glenn D. Rudebusch, 1987. "Scoring the leading indicators," Special Studies Papers 206, Board of Governors of the Federal Reserve System (U.S.).
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