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A Reduced Rank Regression Approach to Coincident and Leading Indexes Building

  • Cubadda, Gianluca

    ()

This paper proposes a reduced rank regression framework for constructing coincident and leading indexes. Based on a formal definition that requires that the first differences of the leading index are the best linear predictor of the first differences of the coincident index, it is shown that the notion of polynomial serial correlation common features can be used to build these composite variables. Concepts and methods are illustrated by an empirical investigation of the US business cycle indicators.

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File URL: http://web.unimol.it/progetti/repec/mol/ecsdps/ESDP04022.pdf
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Paper provided by University of Molise, Dept. EGSeI in its series Economics & Statistics Discussion Papers with number esdp04022.

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Length: 28 pages
Date of creation: 24 Sep 2004
Date of revision:
Handle: RePEc:mol:ecsdps:esdp04022
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  1. Maximo Camacho & Gabriel Perez-Quiros, 2002. "This is what the leading indicators lead," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(1), pages 61-80.
  2. Robert F. Engle & Sharon Kozicki, 1990. "Testing For Common Features," NBER Technical Working Papers 0091, National Bureau of Economic Research, Inc.
  3. Hecq, Alain & Palm, Franz C & Urbain, Jean-Pierre, 2000. " Permanent-Transitory Decomposition in VAR Models with Cointegration and Common Cycles," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(4), pages 511-32, September.
  4. Cubadda, Gianluca & Hecq, Alain, 2001. "On non-contemporaneous short-run co-movements," Economics Letters, Elsevier, vol. 73(3), pages 389-397, December.
  5. Camba-Mendez, Gonzalo, et al, 2003. "Tests of Rank in Reduced Rank Regression Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 145-55, January.
  6. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 95-156 National Bureau of Economic Research, Inc.
  7. Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
  8. Anders Rahbek & Rocco Mosconi, 1999. "Cointegration rank inference with stationary regressors in VAR models," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 76-91.
  9. Cubadda, Gianluca, 1999. "Common Cycles in Seasonal Non-stationary Time Series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(3), pages 273-91, May-June.
  10. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, March.
  11. Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," The Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January.
  12. Rebecca A Emerson & David Hendry, 1994. "An evaluation of forecasting using leading indicators," Economics Papers 5., Economics Group, Nuffield College, University of Oxford.
  13. Wecker, William E, 1979. "Predicting the Turning Points of a Time Series," The Journal of Business, University of Chicago Press, vol. 52(1), pages 35-50, January.
  14. n/a, 2001. "A Comparison of Personal Sector Saving Rates in the UK, US and Italy," NIESR Discussion Papers 150, National Institute of Economic and Social Research.
  15. Proietti, Tommaso, 1997. "Short-Run Dynamics in Cointegrated Systems," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(3), pages 405-22, August.
  16. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 393-95, October.
  17. Granger, Clive W.J. & YOON, GAWON, 2001. "Self-Generating Variables in a Cointegrated VAR Framework," University of California at San Diego, Economics Working Paper Series qt6010k0xn, Department of Economics, UC San Diego.
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