Forecasting linear dynamical systems using subspace methods

DOI10.1111/j.1467-9892.2010.00704.xzbMath1294.62219OpenAlexW1596686560MaRDI QIDQ5495692

Alfredo García-Hiernaux

Publication date: 6 August 2014

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

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2010.00704.x

zbMATH Keywords

subspace methodsforecastingcombining forecasts

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) Time series analysis of dynamical systems (37M10)

Uses Software

• ARfit

Cites Work

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• Estimating the dimension of a model
• Estimating cointegrated systems using subspace algorithms
• Consistency and relative efficiency of subspace methods
• A note on modelling core inflation for the UK using a new dynamic factor estimation method and a large disaggregated price index dataset
• Business cycle analysis and VARMA models
• Estimating the system order by subspace methods
• Fast estimation methods for time-series models in state–space form
• Unit roots and cointegration modelling through a family of flexible information criteria
• Algorithm 808
• Comparing the CCA Subspace Method to Pseudo Maximum Likelihood Methods in the case of No Exogenous Inputs
• ESTIMATING LINEAR DYNAMICAL SYSTEMS USING SUBSPACE METHODS

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