Selecting optimal multistep predictors for autoregressive processes of unknown order.

DOI10.1214/009053604000000148zbMath1048.62088arXivmath/0406433OpenAlexW2038074608MaRDI QIDQ1879949

Ching-Kang Ing

Publication date: 15 September 2004

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0406433

zbMATH Keywords

model selectionautoregressive processaccumulated prediction erroroptimal multistep predictor

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Strong limit theorems (60F15)

Related Items (12)

On the selection of forecasting models ⋮ Time-series estimation of the effects of natural experiments ⋮ Least-squares forecast averaging ⋮ Negative Moment Bounds for Stochastic Regression Models with Deterministic Trends and Their Applications to Prediction Problems ⋮ Toward optimal multistep forecasts in non-stationary autoregressions ⋮ Simultaneous confidence bands for sequential autoregressive fitting ⋮ Forecasting time series of economic processes by model averaging across data frames of various lengths ⋮ Model averaging based on leave-subject-out cross-validation for vector autoregressions ⋮ Direct multiperiod forecasting for algorithmic trading ⋮ Averaging estimators for autoregressions with a near unit root ⋮ Predictor Selection for Positive Autoregressive Processes ⋮ The Multistep Beveridge–Nelson Decomposition

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