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Fresh perspectives on unobservable variables: Data decomposition of the Kalman smoother

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    Macroeconomics makes extensive use of concepts for which there are no observed data. Empirical estimates of such unobservable variables - core inflation is one example - have to be estimated from observed data. The data decomposition tool helps identify the contribution of each piece of observed data to the estimate of the unobservable variable.

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    File URL: http://www.rbnz.govt.nz/research_and_publications/analytical_notes/2013/an2013_09.pdf
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    Paper provided by Reserve Bank of New Zealand in its series Reserve Bank of New Zealand Analytical Notes series with number AN2013/09.

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    Length: 34 p.
    Date of creation: Dec 2013
    Date of revision:
    Handle: RePEc:nzb:nzbans:2013/09
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    1. Cogley, Timothy & Nason, James M., 1995. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 253-278.
    2. Riccardo Cristadoro & Mario Forni & Lucrezia Reichlin & Giovanni Veronese, 2005. "A core inflation indicator for the Euro area," ULB Institutional Repository 2013/10131, ULB -- Universite Libre de Bruxelles.
    3. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-47, July-Sept.
    4. Michal Andrle, 2013. "Understanding DSGE Filters in Forecasting and Policy Analysis," IMF Working Papers 13/98, International Monetary Fund.
    5. Michal Andrle, 2013. "What Is in Your Output Gap? Unified Framework & Decomposition into Observables," IMF Working Papers 13/105, International Monetary Fund.
    6. Forni, Mario & Lippi, Marco, 2000. "The Generalized Dynamic Factor Model: Representation Theory," CEPR Discussion Papers 2509, C.E.P.R. Discussion Papers.
    7. Alejandro Justiniano & Bruce Preston, 2006. "Can Structural Small Open Economy Models Account for the Influence of Foreign Disturbances?," 2006 Meeting Papers 479, Society for Economic Dynamics.
    8. Harvey, A C, 1985. "Trends and Cycles in Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(3), pages 216-27, June.
    9. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    10. Michael Kirker, 2010. "What drives core inflation? A dynamic factor model analysis of tradable and nontradable prices," Reserve Bank of New Zealand Discussion Paper Series DP2010/13, Reserve Bank of New Zealand.
    11. 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.
    12. King, R.G. & Rebelo, S.T., 1989. "Low Frequency Filtering And Real Business Cycles," RCER Working Papers 205, University of Rochester - Center for Economic Research (RCER).
    13. Clark, Peter K, 1987. "The Cyclical Component of U.S. Economic Activity," The Quarterly Journal of Economics, MIT Press, vol. 102(4), pages 797-814, November.
    14. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
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