Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series

DOI10.1093/biomet/asz007zbMath1442.62206arXiv1704.02519OpenAlexW2962888970WikidataQ92092125 ScholiaQ92092125MaRDI QIDQ4973625

Ali Shojaie, Alex Tank, Emily B. Fox

Publication date: 28 November 2019

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1704.02519

zbMATH Keywords

time seriessubsamplingmixed frequencystructural vector autoregressive modelnon-Gaussian error

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Generalized linear models (logistic models) (62J12) Missing data (62D10)

Related Items (4)

A constraint optimization approach to causal discovery from subsampled time series data ⋮ Discrete mixtures of normals pseudo maximum likelihood estimators of structural vector autoregressions ⋮ Locally robust inference for non-Gaussian linear simultaneous equations models ⋮ Invariant Causal Prediction for Sequential Data

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