Measuring Core Inflation by Multivariate Structural Time Series Models
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.
|Date of creation:||31 May 2006|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.ceistorvergata.it
More information through EDIRC
|Order Information:|| Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma|
Web: http://www.ceistorvergata.it Email:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543, March.
- Tom Doan, . "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
- 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.
- A. C. Harvey & Siem Jan Koopman, 2000. "Computing Observation Weights for Signal Extraction and Filtering," Econometric Society World Congress 2000 Contributed Papers 0888, Econometric Society.
- Michael F. Bryan & Stephen G. Cecchetti, 1993.
"Measuring Core Inflation,"
NBER Working Papers
4303, National Bureau of Economic Research, Inc.
- Harvey, A.C. & Koopman, S.J.M., 1999.
"Signal Extraction and the Formulation of Unobserved Components Models,"
1999-44, Tilburg University, Center for Economic Research.
- 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.
- Michael F. Bryan & Stephen G. Cecchetti & Rodney L. Wiggins II, 1997.
"Efficient inflation estimation,"
9707, Federal Reserve Bank of Cleveland.
- Mark A. Wynne, 2008.
"Core inflation: a review of some conceptual issues,"
Federal Reserve Bank of St. Louis, issue May, pages 205-228.
- Mark A. Wynne, 1999. "Core inflation: a review of some conceptual issues," Working Papers 9903, Federal Reserve Bank of Dallas.
- Wynne, Mark A., 1999. "Core inflation: a review of some conceptual issues," Working Paper Series 0005, European Central Bank.
When requesting a correction, please mention this item's handle: RePEc:rtv:ceisrp:83. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Barbara Piazzi)
If references are entirely missing, you can add them using this form.