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Single source of error state space approach to the Beveridge Nelson decomposition

Author

Listed:
  • Heather M. Anderson
  • Chin Nam Low
  • Ralph Snyder

Abstract

A well known property of the Beveridge Nelson decomposition is that the innovations in the permanent and transitory components are perfectly correlated. We use a single source of error state space model to exploit this property and perform a Beveridge Nelson decomposition. The single source of error state space approach to the decomposition is computationally simple and it incorporates the direct estimation of the long-run multiplier.

Suggested Citation

  • Heather M. Anderson & Chin Nam Low & Ralph Snyder, 2005. "Single source of error state space approach to the Beveridge Nelson decomposition," CAMA Working Papers 2005-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2005-11
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    2. Günes Kamber & James Morley & Benjamin Wong, 2018. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 550-566, July.
    3. Kamil, Nazrol & Masih, Mansur, 2016. "Shari’ah (islamic)compliant investments in Malaysia: influences of selected stock indices and their trend/cycle decomposition equity," MPRA Paper 100955, University Library of Munich, Germany.
    4. Kum Hwa Oh & Eric Zivot & Drew Creal, 2006. "The Relationship between the Beveridge-Nelson Decomposition andUnobserved Component Models with Correlated Shocks," Working Papers UWEC-2006-16-FC, University of Washington, Department of Economics.
    5. Chin Nam Low & Heather Anderson & Ralph Snyder, 2006. "Beveridge-Nelson Decomposition with Markov Switching," Melbourne Institute Working Paper Series wp2006n14, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    6. Pagan, Adrian & Robinson, Tim, 2022. "Excess shocks can limit the economic interpretation," European Economic Review, Elsevier, vol. 145(C).
    7. Blasques, F. & van Brummelen, J. & Gorgi, P. & Koopman, S.J., 2024. "A robust Beveridge–Nelson decomposition using a score-driven approach with an application," Economics Letters, Elsevier, vol. 236(C).
    8. Luis A.V. Catão & Adrian Pagan, 2011. "The Credit Channel and Monetary Transmission in Brazil and Chile: A Structured VAR Approach," Central Banking, Analysis, and Economic Policies Book Series, in: Luis Felipe Céspedes & Roberto Chang & Diego Saravia (ed.),Monetary Policy under Financial Turbulence, edition 1, volume 16, chapter 5, pages 105-144, Central Bank of Chile.
    9. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    10. de Silva, Ashton & Hyndman, Rob J. & Snyder, Ralph, 2009. "A multivariate innovations state space Beveridge-Nelson decomposition," Economic Modelling, Elsevier, vol. 26(5), pages 1067-1074, September.
    11. A. R. Pagan & Mr. Douglas Laxton & Mr. Luis Catão, 2008. "Monetary Transmission in an Emerging Targeter: The Case of Brazil," IMF Working Papers 2008/191, International Monetary Fund.
    12. Philip Liu, 2007. "Stabilizing The Australian Business Cycle: Good Luck Or Good Policy?," CAMA Working Papers 2007-24, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. Oh, Kum Hwa & Zivot, Eric & Creal, Drew, 2008. "The relationship between the Beveridge-Nelson decomposition and other permanent-transitory decompositions that are popular in economics," Journal of Econometrics, Elsevier, vol. 146(2), pages 207-219, October.
    14. Basistha, Arabinda & Kurov, Alexander, 2010. "Estimating earnings trend using unobserved components framework," Economics Letters, Elsevier, vol. 107(1), pages 55-57, April.
    15. Chew Lian Chua & G. C. Lim & Sarantis Tsiaplias, 2012. "A latent variable approach to forecasting the unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(3), pages 229-244, April.
    16. M. Dungey & J. P. A. M. Jacobs & J. Tian & S. van Norden, 2013. "On the correspondence between data revision and trend-cycle decomposition," Applied Economics Letters, Taylor & Francis Journals, vol. 20(4), pages 316-319, March.
    17. Adrian Pagan & Tim Robinson, 2020. "Too many shocks spoil the interpretation," CAMA Working Papers 2020-28, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Mardi Dungey & Jan P.A.M. Jacobs & Jing Jian & Simon van Norden, 2013. "Trend-Cycle Decomposition: Implications from an Exact Structural Identification," CIRANO Working Papers 2013s-23, CIRANO.
    19. Heather M Anderson & Farshid Vahid, 2010. "VARs, Cointegration and Common Cycle Restrictions," Monash Econometrics and Business Statistics Working Papers 14/10, Monash University, Department of Econometrics and Business Statistics.
    20. Maddalena Cavicchioli, 2023. "Trend and cycle decomposition of Markov switching (co)integrated time series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1381-1406, December.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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