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Band spectral estimation for signal extraction

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  • Proietti, Tommaso

Abstract

The paper evaluates the potential of band spectral estimation for extracting signals in economic time series. Two situations are considered. The first deals with trend extraction when the original data have been permanently altered by routine operations, such as prefiltering, temporal aggregation and disaggregation, and seasonal adjustment, which modify the high frequencies properties of economic time series. The second is when the measurement model is only partially specified, in that it aims at fitting the series in a particular frequency range, e.g. at interpreting the long run behaviour. These issues are illustrated with reference to a simple structural model, namely the random walk plus noise model.

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Bibliographic Info

Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 25 (2008)
Issue (Month): 1 (January)
Pages: 54-69

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Handle: RePEc:eee:ecmode:v:25:y:2008:i:1:p:54-69

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Web page: http://www.elsevier.com/locate/inca/30411

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  1. Engle, Robert F, 1978. "Testing Price Equations for Stability across Spectral Frequency Bands," Econometrica, Econometric Society, vol. 46(4), pages 869-81, July.
  2. Lawrence J. Christiano & Robert J. Vigfusson, 2001. "Maximum likelihood in the frequency domain: the importance of time-to-plan," Working Paper 0106, Federal Reserve Bank of Cleveland.
  3. Francis X. Diebold & Lee E. Ohanian & Jeremy Berkowitz, 1995. "Dynamic Equilibrium Economies: A Framework for Comparing Models and Data," NBER Technical Working Papers 0174, National Bureau of Economic Research, Inc.
  4. Tommaso Proietti, 2004. "Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited," Econometrics 0411011, EconWPA.
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  7. Jaeger, Albert, 1992. "Does Consumption Take a Random Walk? Some Evidence from Macroeconomic Forecasting Data," The Review of Economics and Statistics, MIT Press, vol. 74(4), pages 607-14, November.
  8. Andrew Harvey & Chia-Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
  9. Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 84-107.
  10. Robinson, Peter M., 1977. "The construction and estimation of continuous time models and discrete approximations in econometrics," Journal of Econometrics, Elsevier, vol. 6(2), pages 173-197, September.
  11. Neil Shephard & Jurgen Doornik & Siem Jan Koopman, 1998. "Statistical algorithms for models in state space using SsfPack 2.2," Economics Series Working Papers 1998-W06, University of Oxford, Department of Economics.
  12. Corbae, D. & Ouliaris, S. & Phillips, P.C.B., 1997. "Band Spectral Regression with Trending Data," Working Papers 97-09, University of Iowa, Department of Economics.
  13. Engle, Robert F, 1974. "Band Spectrum Regression," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 1-11, February.
  14. Tommaso Proietti, 2005. "Forecasting and signal extraction with misspecified models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 539-556.
  15. D.S.G. Pollock, 2007. "Investigating Economic Trends And Cycles," Discussion Papers in Economics 07/17, Department of Economics, University of Leicester, revised Apr 2008.
  16. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-75, November.
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Cited by:
  1. D.S.G. Pollock, 2010. "Oversampling of stochastic processes," Working Papers 44, Department of Applied Econometrics, Warsaw School of Economics.

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