ノイズを含む非定常経済時系列のためのSIMLフィルタリング法
The SIML Filtering Method for Noisy Non-stationary Economic Time Series
JSS Research Series in Statistics
Kunitomo, Naoto
Sato, Seisho
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In this book, we explain the development of a new filtering method to estimate the hidden states of random variables for multiple non-stationary time series data. This method is particularly helpful in analyzing small-sample non-stationary macro-economic time series. The method is based on the frequency-domain application of the separating information maximum likelihood (SIML) method, which was proposed by Kunitomo, Sato, and Kurisu (Springer, 2018) for financial high-frequency time series.