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Spectral Analysis for Economic Time Series

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Abstract

The last ten years have witnessed an increasing interest of the econometrics community in spectral theory. In fact, decomposing the series evolution in periodic contributions allows a more insightful view of its structure and on its cyclical behavior at different time scales. In this paper I concisely broach the issues of cross-spectral analysis and filtering, dwelling in particular upon the windowed filter (Iacobucci and Noullez 2002). In order to show the usefulness of these tools, I present an application to real data, namely to US unemployment and inflation. I show how cross spectral analysis and filtering can be used to find correlation between them (i.e. the Phillips curve) in some specific frequency bands, even if it does not appear in raw data.

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Bibliographic Info

Paper provided by Observatoire Francais des Conjonctures Economiques (OFCE) in its series Documents de Travail de l'OFCE with number 2003-07.

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Date of creation: 2003
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Publication status: Forthcoming in "New Tools for Quantitative Analysis of Economic Dynamics"
Handle: RePEc:fce:doctra:0307

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Keywords: spectral and cross spectral analysis; frequency selective filters; Phillips curve.;

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References

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  1. Haldane, Andrew & Quah, Danny, 1999. "UK Phillips curves and monetary policy," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 259-278, October.
  2. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, 05.
  3. Luca Benati, 2001. "Band-pass filtering, cointegration, and business cycle analysis," Bank of England working papers 142, Bank of England.
  4. Christian J. Murray, 2003. "Cyclical Properties of Baxter-King Filtered Time Series," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 472-476, May.
  5. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
  6. 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.
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Cited by:
  1. Papageorgiou, Theofanis & Michaelides, Panayotis G. & Milios, John G., 2010. "Business cycles synchronization and clustering in Europe (1960-2009)," Journal of Economics and Business, Elsevier, vol. 62(5), pages 419-470, September.
  2. Shruthi Jayaram & Ila Patnaik & Ajay Shah, 2009. "Examining the decoupling hypothesis for India," Trade Working Papers 22972, East Asian Bureau of Economic Research.
  3. Michaelides, Panayotis G. & Papageorgiou, Theofanis & Vouldis, Angelos T., 2013. "Business cycles and economic crisis in Greece (1960–2011): A long run equilibrium analysis in the Eurozone," Economic Modelling, Elsevier, vol. 31(C), pages 804-816.
  4. Esteban Perez Caldentey & Matias Vernengo, 2013. "Wage and Profit-led Growth: The Limits to Neo-Kaleckian Models and a Kaldorian Proposal," Economics Working Paper Archive wp_775, Levy Economics Institute.
  5. Pomenková, Jitka & Fidrmuc, Jarko & Korhonen, Iikka, 2014. "China and the World economy: Wavelet spectrum analysis of business cycles," BOFIT Discussion Papers 5/2014, Bank of Finland, Institute for Economies in Transition.
  6. Leon, Costas & Eeckels, Bruno, 2009. "A Dynamic Correlation Approach of the Swiss Tourism Income," MPRA Paper 15215, University Library of Munich, Germany.
  7. Svatopluk Kapounek & Jitka Pomenkova, 2012. "Spurious synchronization of business cycles: Dynamic correlation analysis of V4 countries," MENDELU Working Papers in Business and Economics 2012-22, Mendel University in Brno, Faculty of Business and Economics.

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