How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation

By A. Inoue, Lutz Kilian. 2005

229 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/00333292b40bcf7ad601f71e94fe1c4f4b537208

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Citations in the corpus

On LASSO for predictive regression (2018, methodology): “In the time series forecasting context, Inoue and Kilian (2008) apply various model selection and model averaging methods to forecast U.S. consumer price inflation.”
Choice of Sample Split in Out-of-Sample Forecast Evaluation (2012, methodology): “We focus on the common case where forecasts are produced from recursively estimated regression models using least squares and forecast accuracy is evaluated using mean squared errors, (e.g., Diebold & Rudebusch (1991), Inoue & Kilian (2008), Patton & Timmermann (2007), and Stock & Watson (2002).)”
Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better? (2007, unknown): “One avenue for future research is to investigate whether alternative techniques for combining forecasts perform better (see Inoue and Kilian, 2005, for a survey and study of one promising technique).”
Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods (2019, unknown): “For example, Inoue and Kilian (2008) considered bagging, factor models, and other linear shrinkage estimators to forecast US inﬂation with a small set of real economic activity indicators.”
Machine Learning Advances for Time Series Forecasting (2020, background): “8 It was popularized in the time-series literature by Inoue and Kilian (2008), who to construct forecasts from multiple regression models with local-to-zero regression parameters and errors subject to possible serial correlation or conditional heteroskedasticity.”
Hierarchical shrinkage priors for dynamic regressions with many predictors (2011, methodology): “These methods include statistical algorithms adopted in econometric forecasting, such as bagging (Inoue and Kilian, 2008), least absolute shrinkage and selection operator (LASSO) (De Mol, Giannone, and Reichlin, 2008), boosting (Bai and Ng, 2007), Bayesian model averaging (Koop and Potter, 2004)…”
Forecasting economic time series using targeted predictors (2008, unknown): “Inoue and Kilian (2008) orthogonalized the data on about 30 variables and used hard thresholding at each bootstrap sample to reduce forecasting variance.”
AN EMPIRICAL COMPARISON OF METHODS FOR FORECASTING USING MANY PREDICTORS (2005, unknown): “estimators. Inoue and Kilian (2004) apply bagging to a forecasting situation like that”
Complete subset regressions (2013, unknown): “Following Inoue and Kilian (2008), we consider a range of shrinkage values γ ∈ {0.”
Neural network ensemble operators for time series forecasting (2014, background): “Recent research has lead to a series of studies involving the application of the Bagging algorithm for forecasting purposes with positive results in many application areas (Inoue and Kilian, 2008; Lee and Yang, 2006; Chen and Ren, 2009; Hillebrand and Medeiros, 2010; Langella et al., 2010).”
Forecasting: theory and practice (2020, background): “, 2016), artificial neural networks (Barrow and Crone, 2016a,b), and other forecast methods (Athanasopoulos et al., 2018; Hillebrand and Medeiros, 2010; Inoue and Kilian, 2008).”
Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version (2013, unknown): “Stock and Watson (2012) describe a general shrinkage representation that covers special cases like pretest, BMA, empirical Bayes, and bagging (Inoue and Kilian, 2008).”
Generalized Shrinkage Methods for Forecasting Using Many Predictors (2012, unknown): “…and Reichlin 2008; Korobilis 2008; Bańbura, Giannone, and Reichlin 2010; Carriero, Kapetanios, and Marcellino 2011), Bayesian model averaging (BMA; Jacobson and Karlsson 2004; Koop and Potter 2004; Wright 2009; Eklund and Karlsson 2007), bagging (BG; Inoue and Kilian 2008), Lasso (Bai and Ng…”
INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS (2018, methodology): “There are also many alternative approaches to model averaging, for example, bagging (Breiman, 1996; Inoue and Kilian, 2008), LASSO (Tibshirani, 1996), adaptive LASSO (Zou, 2006), and the model confidence set (Hansen et al.”