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Extracting nonlinear signals from several economic indicators

  • Camacho, Maximo
  • Pérez-Quirós, Gabriel
  • Poncela, Pilar

We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non- linear multivariate specification (one-step approach) with the shortcut of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US data.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 8865.

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Date of creation: Feb 2012
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Handle: RePEc:cpr:ceprdp:8865
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  1. Chauvet, Marcelle & Piger, Jeremy, 2008. "A Comparison of the Real-Time Performance of Business Cycle Dating Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 42-49, January.
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  10. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
  11. James Morley & Jeremy M. Piger, 2005. "The importance of nonlinearity in reproducing business cycle features," Working Papers 2004-032, Federal Reserve Bank of St. Louis.
  12. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
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  14. Paap, R. & Segers, R. & van Dijk, D.J.C., 2007. "Do leading indicators lead peaks more than troughs?," Econometric Institute Research Papers EI 2007-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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