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Measuring Core Inflation by Multivariate Structural Time Series Models

  • Tommaso Proietti

    ()

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

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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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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  1. 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.
  2. Michael F. Bryan & Stephen G. Cecchetti, 1993. "Measuring Core Inflation," NBER Working Papers 4303, National Bureau of Economic Research, Inc.
  3. Michael F. Bryan & Stephen G. Cecchetti & Rodney L. Wiggins II, 1997. "Efficient inflation estimation," Working Paper 9707, Federal Reserve Bank of Cleveland.
  4. Mark A. Wynne, 2008. "Core inflation: a review of some conceptual issues," Review, Federal Reserve Bank of St. Louis, issue May, pages 205-228.
  5. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May.
  6. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, March.
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