Optimal signal extraction with correlated components

DOI10.1515/jtse-2013-0016zbMath1499.62340OpenAlexW2002946498MaRDI QIDQ1695657

Tucker S. McElroy, Agustin Maravall

Publication date: 7 February 2018

Published in: Journal of Time Series Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/jtse-2013-0016

zbMATH Keywords

seasonalitytime seriesARIMAnonstationary

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inference from stochastic processes and spectral analysis (62M15)

Related Items (1)

Predictability, real time estimation, and the formulation of unobserved components models

Uses Software

• R

Cites Work

• The relationship between the Beveridge-Nelson decomposition and other permanent-transitory decompositions that are popular in economics
• Signal extraction from nonstationary time series
• An iterated parametric approach to nonstationary signal extraction
• Time series: theory and methods.
• The beveridge-nelson decomposition: Properties and extensions
• MATRIX FORMULAS FOR NONSTATIONARY ARIMA SIGNAL EXTRACTION
• An ARIMA-Model-Based Approach to Seasonal Adjustment
• INITIALIZING THE KALMAN FILTER FOR NONSTATIONARY TIME SERIES MODELS
• Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models
• Computation of asymmetric signal extraction filters and mean squared error for ARIMA component models
• State Space and Unobserved Component Models
• Trend–Cycle Decompositions with Correlated Components
• On the fitting of multivariate autoregressions, and the approximate canonical factorization of a spectral density matrix
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