Robustify Financial Time Series Forecasting with Bagging
DOI10.1080/07474938.2013.825142zbMath1491.62159OpenAlexW2027965814MaRDI QIDQ5080461
Liangjun Su, Sainan Jin, Aman Ullah
Publication date: 31 May 2022
Published in: Econometric Reviews (Search for Journal in Brave)
Full work available at URL: https://ink.library.smu.edu.sg/soe_research/1428
Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Nonparametric estimation (62G05) Nonparametric statistical resampling methods (62G09)
Related Items (4)
Cites Work
- Unnamed Item
- Unnamed Item
- Bagging predictors
- Bagging binary and quantile predictors for time series
- Least-squares forecast averaging
- Handbook of economic forecasting. Volume 1
- On bagging and nonlinear estimation
- Data-driven bandwidth choice for density estimation based on dependent data
- Heuristics of instability and stabilization in model selection
- Analyzing bagging
- Nonparametric Estimation and Identification of Nonlinear ARCH Time Series Strong Convergence and Asymptotic Normality: Strong Convergence and Asymptotic Normality
- How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation
- Automatic Block-Length Selection for the Dependent Bootstrap
- COMBINING FORECASTING PROCEDURES: SOME THEORETICAL RESULTS
- UNIFORM CONVERGENCE RATES OF KERNEL ESTIMATORS WITH HETEROGENEOUS DEPENDENT DATA
This page was built for publication: Robustify Financial Time Series Forecasting with Bagging
