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Frequency Regression and Smoothing for Noisy Nonstationary Time Series

Author

Listed:
  • Seisho Sato

    (University of Tokyo)

  • Naoto Kunimoto

    (Tokyo Keizai University)

Abstract

We develop a new regression method called frequency regression and smoothing. This method is based on the separating information maximum likelihood developed by Kunitomo and Sato (2021) and Sato and Kunitomo (2020) for estimating the hidden states of random variables and handling noisy nonstationary (small sample) time series data. Many economic time series include not only the trend-cycle, seasonal, and measurement error components, but also factors such as structural breaks, abrupt changes, trading-day effects, and institutional changes. Frequency regression and smoothing can be applied to handle such factors in nonstationary time series. The proposed method is simple and applicable to several problems when analyzing nonstationary economic time series and handling seasonal adjustments. An illustrative empirical analysis of the macroconsumption in Japan is provided.

Suggested Citation

  • Seisho Sato & Naoto Kunimoto, 2021. "Frequency Regression and Smoothing for Noisy Nonstationary Time Series," CARF F-Series CARF-F-519, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    • Handle: RePEc:cfi:fseres:cf519
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    References listed on IDEAS

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    1. Engle, Robert F, 1974. "Band Spectrum Regression," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 1-11, February.
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