Forecasting U.S. Inflation by Bayesian Model Averaging

By Jonathan H. Wright. 2003

131 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b091007bff1f0386894a4d9ee463320e9d75db35

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In Economics and Econometrics

Citations in the corpus

Advances in Forecasting Under Instability (2012, unknown): “Wright (2009) nds that BMA provides better out-of-sample forecasts of U.S. in ation than equal weight forecast averaging.”
Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach, Second Version (2013, unknown): “The BMA has been widely used in econometric applications, including Sala-iMartin, Doppelhofer, and Miller (2004), Brock and Durlauf (2001), Brock, Durlauf, and West (2003), Avramov (2002), Fernandez, Lay, and Steel (2001a,b), Garratt, Lee, Pesaran, and Shin (2003), and Wright (2008, 2009).”
Bayesian VARs : specification choices and forecast accuracy (2011, unknown): “We follow other studies such as Jacobson and Karlsson (2004), Koop (2013) and Wright (2009) in applying direct methods to multi-step forecast horizons.”
Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging (2012, unknown): “BMA has been shown to outperform simple skill-based averaging in seasonal forecasting (Casanova and Ahrens 2009), and to perform well in forecasting and predictions in general (e.g., Raftery et al. 2005; Wright 2009).”
How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation (2005, unknown): “This empirical example is in the spirit of recent work by Stock andWatson (1999), Bernanke and Boivin (2003), Forni et al. (2003), and Wright (2003b), among others.”
Prior Selection for Vector Autoregressions (2015, background): “…the model generates zero forecast errors, which can 1 A number of papers have subsequently followed either the first (Robertson & Tallman, 1999; Wright, 2009; Giannone et al., 2014) or the second strategy (Giannone, Lenza, & Reichlin, 2008; Bloor & Matheson, 2011; Carriero, Kapetanios, &…”
Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts (2008, unknown): “6 As Wright (2003) actually uses a coefficient prior of 0, our use of the recursive prior requires a simple adjustment to the S term that enters the posterior probability.”
Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better? (2007, background): “…forecast combinations typically provide better forecasts than individual forecasting models.6 For inflation forecasts, Stock and Watson (1999) and Wright (2004), among others, show that combined forecasts using real activity and financial indicators are usually more accurate than individual…”
Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components? (2008, unknown): “A related literature has explored the performance of Bayesian model averaging for forecasting (Koop and Potter, 2003; Stock and Watson, 2006, 2005a; Wright, 2003) but, surprisingly, few papers explore the performance of Bayesian regression in forecasting with high-dimensional data.”
Generalized Shrinkage Methods for Forecasting Using Many Predictors (2012, methodology): “can be improved upon using other many-predictor methods, including high-dimensional Bayesian vector autoregression (VAR; Andersson and Karlsson 2008; De Mol, Giannone, and Reichlin 2008; Korobilis 2008; Bañbura, Giannone, and Reichlin 2010; Carriero, Kapetanios, and Marcellino 2011), Bayesian model ”
Intelligence, Human Capital, and Economic Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach (2005, unknown): “The SDM approach is a form of Bayesian model averaging, an approach that is increasing in popularity among empirical economists who are concerned with model uncertainty (inter alia, Wright, 2003; Koop and Potter, 2003; Milani, 2004).”