Summary
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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References
Anderson, T. W. An Introduction to Multivariate Statistical Analysis. Second Edition, John Wiley & Sons, 1984.
Bell, W. Signal Extraction for Nonstationary Time Series. The Annals of Statistics, 12, 646–664, 1984.
Bryan M. F. and S. G. Cecchetti. Measuring Core Inflation. In N. G. Mankiw, editor, Monetary Policy. Chicago: University of Chicago Press, 1994.
Bryan M. F., S. G. Cecchetti and R. L. Wiggins II. Efficient Inflation Estimation. NBER Working Paper n. 6183. , Cambridge, MA., 1997.
Doornik, J. A. Ox: An Object-Oriented Matrix Programming Language. London: Timberlake Consultants Ltd., 1999.
Durbin, J. and S. J. Koopman. Time Series Analysis by State Space Methods. Oxford University Press, Oxford, 2001.
Enns, P. G., J. A. Machak, W. A. Spivey and W. J. Wrobleski. Forecasting applications of an adaptive multiple exponential smoothing model. Management Science, 28, 1035–1044, 1982.
Fernandez F. J. and A. C. Harvey. Seemingly unrelated time series equations and a test for homogeneity. Journal of Business and Economic Statistics, 8, 71–81, 1990.
Harvey A. C. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge, 1989.
Harvey A. C. and S. J. Koopman. Signal extraction and the formulation of unobserved components models. Econometrics Journal, 3, 84–107, 2000.
Koopman S. J. and A. C. Harvey. Computing observation weights for signal extraction and filtering. Journal of Economic Dynamics and Control, 27, 1317–33, 2003.
Koopman S. J., A. C. Harvey, J. A. Doornik and N. Shephard. STAMP 6: Structural Time Series Analysis Modeller and Predictor. London: Timberlake Consultants Ltd., 2000.
Marshall P. Estimating Time-Dependent Means in Dynamic Models for Cross-sections of Time Series. Empirical Economics, 17, 25–33, 1992.
Nyblom J. and A. C. Harvey. Tests of Common Stochastic Trends. Econometric Theory, 16, 76–99, 2003.
Quah D. and S. Vahey. Measuring Core Inflation. Economic Journal, 105, 1130–1144, 1995.
Selvanathan, E. A. and D. S. Prasada Rao. Index Numbers. A Stochastic Approach. Macmillan, London, 1994.
Stock J. H. and M. W. Watson. A Probability Model of Coincident Economic Indicators. In K. Lahiri and G. H. Moore, editors, Leading Economic Indicators: New Approaches and Forecasting Records. Cambridge University Press, New York, pp. 63–85, 1991.
Whittle P. Prediction and Regulation by Least Squares Methods. 2nd ed. Oxford: Blackwell, 1983.
Wynne M. A. Core Inflation: A Review of some Conceptual Issues. ECB Working Paper No. 5.5. European Central Bank, Frankfurt am Main, 1999.
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Proietti, T. (2007). Measuring Core Inflation by Multivariate Structural Time Series Models. In: Kontoghiorghes, E.J., Gatu, C. (eds) Optimisation, Econometric and Financial Analysis. Advances in Computational Management Science, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36626-1_10
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