The SIML Filtering Method for Noisy Non-stationary Economic Time Series
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 frequ...
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| Autori principali: | , |
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| Ente Autore: | |
| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2025.
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| Edizione: | 1st ed. 2025. |
| Serie: | JSS Research Series in Statistics,
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| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-981-96-0882-9 |
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Sommario:
- Introduction
- Macro Examples and Non-Stationary Errors-in-Variables Model
- The SIML Filtering Method
- Comparing Estimation Methods of Non-stationary Errors-in Variables Models
- Frequency Regression and Smoothing for Noisy Non-stationary Multivariate Time Series.