Forecasting a Nonstationary Time Series Using a Mixture of Stationary and Nonstationary Factors as Predictors
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Publication:6150354
DOI10.1080/07350015.2023.2166048OpenAlexW4319841746MaRDI QIDQ6150354
J. T. Gao, Unnamed Author, Mervyn J. Silvapullé, Paramsothy Silvapulle
Publication date: 6 March 2024
Published in: Journal of Business & Economic Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/07350015.2023.2166048
panel datahigh dimensional dataindustrial productiongross domestic productfactor augmented regressiongenerated factors
Cites Work
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- Forecasting with factor-augmented regression: a frequentist model averaging approach
- Efficient estimation of nonstationary factor models
- Determining the number of factors when the number of factors can increase with sample size
- Testing for structural breaks in dynamic factor models
- A time-varying diffusion index forecasting model
- On time-varying factor models: estimation and testing
- Estimating cross-section common stochastic trends in nonstationary panel data
- Principal components estimation and identification of static factors
- Testing for structural stability of factor augmented forecasting models
- Bootstrapping factor-augmented regression models
- Adaptive forecasting in the presence of recent and ongoing structural change
- Folklore Theorems, Implicit Maps, and Indirect Inference
- Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions
- Forecasting Using Principal Components From a Large Number of Predictors
- Inferential Theory for Factor Models of Large Dimensions
- Determining the Number of Factors in Approximate Factor Models
- Model selection in factor-augmented regressions with estimated factors
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