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Measuring Core Inflation by Multivariate Structural Time Series Models

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  • Tommaso Proietti

    ()
    (Università degli Studi di Udine - Dipartimento di Scienze Statistiche)

Abstract

The measurement of core inflation can be carried out by optimal signal extraction techniques based on the multivariate local level model, by imposing suitable restrictions on its parameters. The various restrictions correspond to several specialisations of the model:the core inflation measure becomes the optimal estimate of the common trend in a multivariate time series of inflation rates for a variety of goods and services, or it becomes a minimum variance linear combination of the inflation rates, or it represents the component generated by the common disturbances in a dynamic error component formulation of the multivariate local level model. Particular attention is given to the characterisation of the optimal weighting functions and to the design of signal extraction filters that can be viewed as two sided exponentially weighted moving averages applied to a cross-sectional average of individual inflation rates. An empirical application relative to U.S. monthly inflation rates for 8 expenditure categories is proposed.

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Bibliographic Info

Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 83.

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Length: 21
Date of creation: 31 May 2006
Date of revision:
Handle: RePEc:rtv:ceisrp:83

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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Related research

Keywords: common trends; dynamic factor analysis; homogeneity; exponential smoothing;

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References

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  1. Harvey, A.C. & Koopman, S.J.M., 1999. "Signal Extraction and the Formulation of Unobserved Components Models," Discussion Paper, Tilburg University, Center for Economic Research 1999-44, Tilburg University, Center for Economic Research.
  2. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 27(7), pages 1317-1333, May.
  3. Michael F. Bryan & Stephen G. Cecchetti & Rodney L. Wiggins II, 1997. "Efficient inflation estimation," Working Paper 9707, Federal Reserve Bank of Cleveland.
  4. Michael F. Bryan & Stephen G. Cecchetti, 1994. "Measuring Core Inflation," NBER Chapters, in: Monetary Policy, pages 195-219 National Bureau of Economic Research, Inc.
  5. Mark A. Wynne, 1999. "Core inflation: a review of some conceptual issues," Working Papers, Federal Reserve Bank of Dallas 9903, Federal Reserve Bank of Dallas.
  6. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198523543, October.
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Cited by:
  1. Terence C. Mills, 2013. "Constructing U.K. Core Inflation," Econometrics, MDPI, Open Access Journal, vol. 1(1), pages 32-52, April.

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