Convolutional autoregressive models for functional time series

DOI10.1016/j.jeconom.2016.05.006zbMath1443.62277OpenAlexW2414365285MaRDI QIDQ308370

Xialu Liu, Han Xiao, Rong Chen

Publication date: 6 September 2016

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

Full work available at URL: https://doi.org/10.1016/j.jeconom.2016.05.006

zbMATH Keywords

nonparametric methodsfunctional time seriessieve estimationspline methods

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional data analysis (62R10) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (7)

Sieve bootstrapping the memory parameter in long-range dependent stationary functional time series ⋮ Functional Time Series Prediction Under Partial Observation of the Future Curve ⋮ Long-Range Dependent Curve Time Series ⋮ Pricing European-type, early-exercise and discrete barrier options using an algorithm for the convolution of Legendre series ⋮ A comparison of Hurst exponent estimators in long-range dependent curve time series ⋮ Varying coefficient functional autoregressive model with application to the U.S. treasuries ⋮ Stock market trend prediction using a functional time series approach

Uses Software

• fda (R)

Cites Work

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