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Computational Statistics

, Volume 24, Issue 3, pp 459–479 | Cite as

An omnibus noise filter

  • Claudio MoranaEmail author
Original Paper

Abstract

A new noise filtering approach, based on flexible least squares (FLS) estimation of an unobserved component local level model, is introduced. The proposed FLS filter has been found to perform well in Monte Carlo analysis, independently of the persistence properties of the data and the size of the signal to noise ratio, ouperforming in general even the Wiener Kolmogorov filter, which, theoretically, is a minimum mean square estimator. Moreover, a key advantage of the proposed filter, relatively to available competitors, is that any persistence property of the data can be handled, without any pretesting, being computationally fast and not demanding, and easy to be implemented as well.

Keywords

Signal–noise decomposition Long memory Structural breaks Flexible least squares Exchange rates volatility 

JEL Classification

C32 

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References

  1. Arino MA, Marmol F (2004) A permanent-transitory decomposition for ARFIMA processes. J Stat Plan Inference 124: 87–97zbMATHCrossRefMathSciNetGoogle Scholar
  2. Baillie RT (1996) Long memory processes and fractional integration in econometrics. J Econom 73: 5–59zbMATHCrossRefMathSciNetGoogle Scholar
  3. Beltratti A, Morana C (2006) Breaks and persistence: macroeconomic causes of stock market volatility. J Econom 131: 151–177CrossRefMathSciNetGoogle Scholar
  4. Beveridge S, Nelson CR (1981) A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. J Monet Econ 7: 151–174CrossRefGoogle Scholar
  5. Engle RF, Granger CWJ (1987) Co-integration and error correction representation. Estimation Test Econom 55: 251–276zbMATHMathSciNetGoogle Scholar
  6. Gonzalo J, Granger C (1995) Estimation of common long-memory components in cointegrated systems. J Bus Econ Stat 13(1): 27–35CrossRefMathSciNetGoogle Scholar
  7. Harvey AC (1998) Long memory in stochastic volatility. In: Knight J, Satchell S (eds) Forecasting volatility in financial markets. Butterworth-Heineman, Oxford, pp 307–320Google Scholar
  8. Harvey AC (1989) Forecasting, structural time series models and the Kalman filter. Cambrdige University Press, CambridgeGoogle Scholar
  9. Kasa K (1992) Common stochastic trends in international stock markets. J Monet Econ 29: 95–124CrossRefGoogle Scholar
  10. Kalaba R, Tesfatsion L (1989) Time-varying linear regression via flexible least squares. Comput Math Appl 17: 1215–1245zbMATHCrossRefMathSciNetGoogle Scholar
  11. Kalaba R, Tesfatsion L (1990a) An organizing principle for dynamic estimation. J Optim Theory Appl 64(3): 445–470CrossRefMathSciNetGoogle Scholar
  12. Kalaba R, Tesfatsion L (1990b) Flexible least squares for approximately linear systems. IEEE Trans Syst Man Cybern 20: 978–989zbMATHCrossRefMathSciNetGoogle Scholar
  13. Kladroba A (2005) Flexible least squares estimation of state space models: an alternative to Kalman filtering? University of Duisburg-Essen, Faculty of Economics, Working Paper, no.149, EssenGoogle Scholar
  14. Marinucci D, Robinson PM (2001) Semiparametric fractional cointegration analysis. J Econom 105: 225–247zbMATHCrossRefMathSciNetGoogle Scholar
  15. Morana C (2002) Common persistent factors in inflation and excess nominal money growth and a new measure of core inflation. Stud Nonlinear Dyn Econom 6(3):art.3–5Google Scholar
  16. Morana C (2004) Frequency domain principal components estimation of fractionally. Cointegrated Process Appl Econ Lett 11: 837–842CrossRefGoogle Scholar
  17. Morana C (2006) A small scale macroeconometric model for Euro-12 area. Econ Model 23(3): 391–426CrossRefGoogle Scholar
  18. Morana C (2007) Multivariate modelling of long memory processes with common components. Comput Stat Data Anal 52: 919–934zbMATHCrossRefMathSciNetGoogle Scholar
  19. Priestley MB (1981) Spectral analysis and time series. Academic Press, LondonzbMATHGoogle Scholar
  20. Robinson PM, Yajima Y (2002) Determination of cointegrating rank in fractional systems. J Econom 106(2): 217–241zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Facoltà di Economia, Dipartimento di Scienze Economiche e Metodi QuantitativiUniversità del Piemonte OrientaleNovaraItaly
  2. 2.International Centre for Economic Research (ICER, Torino)TorinoItaly

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