A robust-filtering method for noisy non-stationary multivariate time series with econometric applications
DOI10.1007/s42081-020-00102-yzbMath1477.62261OpenAlexW3119178728MaRDI QIDQ825334
Publication date: 17 December 2021
Published in: Japanese Journal of Statistics and Data Science (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s42081-020-00102-y
trend-cycleSIMLerrors-variables modelsFourier-inversionmacro-economic data in JapanMüller-Watson methodnoisy non-stationary time seriesreal-valued orthogonal processrobust-filteringseasonality and noise
Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)
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Cites Work
- Separating information maximum likelihood method for high-frequency financial data
- Estimating linear statistical relationships
- Time series: Theory and methods
- Comparing estimation methods of non-stationary errors-in-variables models
- Term structure models during the global financial crisis: a parsimonious text mining approach
- Introduction to Time Series Modeling
- Likelihood-Based Inference in Cointegrated Vector Autoregressive Models
- Long-Run Covariability
- Co-Integration and Error Correction: Representation, Estimation, and Testing
- An Harmonic Analysis of Nonstationary Multivariate Economic Processes
- A SMOOTHING METHOD THAT LOOKS LIKE THE HODRICK–PRESCOTT FILTER
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