Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version

By Xu Cheng, B. Hansen. 2013

141 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0bdca23f93495b1017316064475bb751aca44807

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Citations in the corpus

Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, methodology): “For more recent references, extensions of DFM forecasting methods, and comparisons to other high-dimensional methods, see Stock and Watson (2012b), D’Agostino and Giannone (2012), Clements (Forthcoming), and Cheng and Hansen (2015).”
Model Averaging and Its Use in Economics (2017, methodology): “FMA was used for forecasting with factor-augmented regression models in Cheng and Hansen (2015). In the context of growth theory, Sala-i-Martin (1997) uses (24), but focuses on the “level of confidence”54, using weights that are either uniform or based on the maximized likelihood.”
Model averaging based on leave-subject-out cross-validation (2016, background): “In Cheng and Hansen (2015), the LsoCV criterion was suggested to average forecasts for factor-augmented regressions, but the corresponding asymptotic optimality was not achieved in that work.”
Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series (2018, methodology): “This PCA+PMAMAR method substantially generalizes the framework of factor-augmented linear regression or autoregression (see Stock andWatson 2002; Bernanke, Boivin, and Eliasz 2005; Bai and Ng 2006; Pesaran, Pick, and Timmermann 2011; Cheng and Hansen 2015) to the general semiparametric framework.”
Macroeconomic forecast accuracy in a data‐rich environment (2019, unknown): “However, even if a single predictor may appear to be randomly selected, we note that categories of predictors are in general well represented over time, as in De Mol et al. (2008). Those variations support our RDRMA technique, which relies on regularization to smartly combine the relevant informatio”