Missing data in time series: a note on the equivalence of the dummy variable and the skipping approaches

DOI10.1016/j.spl.2007.05.031zbMath1130.62094OpenAlexW2019587159MaRDI QIDQ2474515

Tommaso Proietti

Publication date: 6 March 2008

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2007.05.031

zbMATH Keywords

smoothingcross-validationKalman filterinfluencedeletion diagnostics

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (2)

Quasi-maximum likelihood estimation of GARCH models in the presence of missing values ⋮ Correcting outliers in GARCH models: a weighted forward approach

Cites Work

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• The diffuse Kalman filter
• Missing observations in ARIMA models: Skipping approach versus additive outlier approach
• Smoothing and Interpolation with the State-Space Model
• Diagnosing Shocks in Time Series
• Disturbance smoother for state space models
• Missing Data in an Autoregressive Model

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