Kalman filter estimation for a regression model with locally stationary errors

DOI10.1016/J.CSDA.2013.01.005zbMath1348.62133OpenAlexW2019439977MaRDI QIDQ333738

Mohammad Hasan, M. Dambrine

Publication date: 31 October 2016

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10533/129415

zbMATH Keywords

long-range dependencelocal stationaritystate space systemnon-stationarityestimation of the statetime-varying models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (5)

Bootstrapping regression models with locally stationary disturbances ⋮ Estimation and prediction of time-varying GARCH models through a state-space representation: a computational approach ⋮ Spatio-temporal analysis with short- and long-memory dependence: a state-space approach ⋮ Estimation of slowly time-varying trend function in long memory regression models ⋮ A Kalman filter method for estimation and prediction of space-time data with an autoregressive structure

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