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Almost periodically correlated time series in business fluctuations analysis

Author

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  • Łukasz Lenart

    (Economic Institute in National Bank of Poland, Department of Mathematics in Cracow University of Economics)

  • Mateusz Pipień

    (Economic Institute in National Bank of Poland, Department of Econometrics and Operations Research in Cracow University of Economics)

Abstract

We propose a non-standard subsampling procedure to make formal statistical inference about the business cycle, one of the most important unobserved feature characterising fluctuations of economic growth. We show that some characteristics of business cycle can be modelled in a non-parametric way by discrete spectrum of the Almost Periodically Correlated (APC) time series. On the basis of estimated characteristics of this spectrum business cycle is extracted by filtering. As an illustration we characterise the man properties of business cycles in industrial production index for Polish economy.

Suggested Citation

  • Łukasz Lenart & Mateusz Pipień, 2012. "Almost periodically correlated time series in business fluctuations analysis," NBP Working Papers 107, Narodowy Bank Polski, Economic Research Department.
  • Handle: RePEc:nbp:nbpmis:107
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    File URL: http://www.nbp.pl/publikacje/materialy_i_studia/107_en.pdf
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    References listed on IDEAS

    as
    1. 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.
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    More about this item

    Keywords

    business cycle; industrial production index; almost periodically correlated time series; subsampling procedure;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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