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Backward Smoothing for Noisy Non-stationary Time Series

Author

Listed:
  • Seisho Sato

    (Graduate School of Economics, University of Tokyo)

  • Naoto Kunitomo

    (Gendai-Finance-Center, Tokyo Keizai University)

Abstract

In this study, we investigate a new smoothing approach to estimate the hidden states of random variables and to handle multiple noisy non-stationary time series data. Kunitomo and Sato (2021) have developed a new method to solve the smoothing problem of hidden random variables, and the resulting separating information maximum likelihood (SIML) method enables the handling of multivariate non-stationary time series. We continue to investigate the filtering problem. In particular, we propose the backward SIML smoothing method and the multi-step smoothing method to address the initial value issue. The resulting filtering methods can be interpreted in the time and frequency domains.

Suggested Citation

  • Seisho Sato & Naoto Kunitomo, 2021. "Backward Smoothing for Noisy Non-stationary Time Series," CARF F-Series CARF-F-517, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    • Handle: RePEc:cfi:fseres:cf517
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    References listed on IDEAS

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    1. Brillinger, David R & Hatanaka, Michio, 1969. "An Harmonic Analysis of Nonstationary Multivariate Economic Processes," Econometrica, Econometric Society, vol. 37(1), pages 131-141, January.
    2. Hirotugu Akaike, 1980. "Seasonal Adjustment By A Bayesian Modeling," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 1-13, January.
    3. Kiyohiko G. Nishimura & Seisho Sato & Akihiko Takahashi, 2019. "Term Structure Models During the Global Financial Crisis: A Parsimonious Text Mining Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(3), pages 297-337, September.
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