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Separating Yolk from White: A Filter based on Economic Properties of Trend and Cycle

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Abstract

This paper proposes a new filter technique to separate trend and cycle based on stylised economic properties of trend and cycle, rather than relying on ad hoc statistical proper-ties such as frequency. Given the theoretical separation between economic growth and business cycle literature, it is necessary to make the measures of trend and cycle match what the respective theories intend to explain. The proposed filter is applied to the long macroeconomic data collected by the Bank of England (1700-2015).

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  • Zhou, Peng, 2017. "Separating Yolk from White: A Filter based on Economic Properties of Trend and Cycle," Cardiff Economics Working Papers E2017/1, Cardiff University, Cardiff Business School, Economics Section.
    • Handle: RePEc:cdf:wpaper:2017/1
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    1. Badinger, Harald, 2010. "Output volatility and economic growth," Economics Letters, Elsevier, vol. 106(1), pages 15-18, January.
    2. Asimakopoulos, Stylianos & Karavias, Yiannis, 2016. "The impact of government size on economic growth: A threshold analysis," Economics Letters, Elsevier, vol. 139(C), pages 65-68.
    3. Harvey, A C, 1985. "Trends and Cycles in Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(3), pages 216-227, June.
    4. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    5. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
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    More about this item

    Keywords

    Filter; Trend; Cycle;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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