Bagging binary and quantile predictors for time series

DOI10.1016/j.jeconom.2005.07.017zbMath1418.62505OpenAlexW2110612433MaRDI QIDQ291866

Yang Yang, Tae-Hwy Lee

Publication date: 10 June 2016

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

Full work available at URL: https://doi.org/10.1016/j.jeconom.2005.07.017

zbMATH Keywords

BMAmajority votingtime seriesforecast combinationbaggingasymmetric cost functionbinary predictionquantile prediction

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05) Economic time series analysis (91B84)

Related Items

Robustify Financial Time Series Forecasting with Bagging, Forecasting vector autoregressions with mixed roots in the vicinity of unity, The Benefits of Bagging for Forecast Models of Realized Volatility, Robust forecast combinations

Uses Software

• quantilogram

Cites Work

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