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On the robustness of stylised business cycle facts for contemporary New Zealand

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  • Viv B. Hall
  • Peter Thomson
  • Stuart McKelvie

Abstract

We assess the robustness of stylised business cycle facts for contemporary New Zealand, traditionally computed from HP1600 trend-filtered data. The merits of these HP1600 estimates are considered, relative to those computed from two loess (local regression) trend filtering methods, one (loess11) chosen to exhibit greater fidelity and the other (loess47) to show more pronounced smoothness. The robustness of our key business cycle facts is further evaluated in terms of simple robust standard error estimates of measures of time-invariant volatility and correlation. Time-varying estimates of these quantities are also investigated. Statistically significant bivariate correlations are established for key real expenditure variables, labour market, fiscal and monetary policy, and some inflation variables, with almost all loess47 absolute magnitudes being somewhat greater than HP1600 magnitudes. Their pro- or counter-cyclicality and their lead/lag relationships are robust across HP1600 and loess47 trend filtering, though not for CPI and non-tradables price level variables.

Suggested Citation

  • Viv B. Hall & Peter Thomson & Stuart McKelvie, 2017. "On the robustness of stylised business cycle facts for contemporary New Zealand," New Zealand Economic Papers, Taylor & Francis Journals, vol. 51(3), pages 193-216, September.
    • Handle: RePEc:taf:nzecpp:v:51:y:2017:i:3:p:193-216
    DOI: 10.1080/00779954.2016.1189956
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    1. Frederick R. Macaulay, 1931. "The Smoothing of Economic Time Series, Curve Fitting and Graduation," NBER Chapters, in: The Smoothing of Time Series, pages 31-42, National Bureau of Economic Research, Inc.
    2. Frederick R. Macaulay, 1931. "The Smoothing of Time Series," NBER Books, National Bureau of Economic Research, Inc, number maca31-1, March.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    4. Frederick R. Macaulay, 1931. "Appendices to "The Smoothing of Time Series"," NBER Chapters, in: The Smoothing of Time Series, pages 118-169, National Bureau of Economic Research, Inc.
    5. Frederick R. Macaulay, 1931. "Introduction to "The Smoothing of Time Series"," NBER Chapters, in: The Smoothing of Time Series, pages 17-30, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 9473, Victoria University of Wellington, School of Economics and Finance.
    2. Viv B Hall & Peter Thomson, 2020. "Does Hamilton’s OLS regression provide a “better alternative†to the Hodrick-Prescott filter? A New Zealand business cycle perspective," CAMA Working Papers 2020-71, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Viv B. Hall & Peter Thomson, 2022. "A boosted HP filter for business cycle analysis:evidence from New Zealand's small open economy," CAMA Working Papers 2022-45, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.

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