Forecasting with Many Predictors

By James H. Stock, Mark W. Watson. 2006

549 citations recorded by the source corpus.

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Citations in the corpus

Are Disaggregate Data Useful for Factor Analysis in Forecasting French GDP (2010, methodology): “Using a large panel of US macroecon mic variables, Stock and Watson (2006) and D’Agostino and Giannone (2007) find that SW and FHLR methods perform similarly, while Boivin and Ng (2005) find that SW’s method largely outperforms the FHLR’s and, in particular, they conjecture that the dynamic…”
Model Averaging and Its Use in Economics (2017, methodology): “This literature on forecast combinations has become quite voluminous, see e.g. Granger (1989) and Stock and Watson (2006) for reviews, while useful surveys of FMA can be found in Wang et al. (2009) and Burnham and Anderson (2002).”
DYNAMIC FACTOR MODELS (2018, background): “…survey is to describe, at a level that is specific enough to be useful to researchers new to the area, the key theoretical results, applications, and empirical findings in the recent literature on DFMs. Bai and Ng (2008) and Stock and Watson (2006) provide complementary surveys of this literature.”
Forecast Combination and Model Averaging Using Predictive Measures (2007, unknown): “…previous work Jacobson and Karlsson (2004) find this approach to work well when the forecast combinations are based on the marginal likelihood, and Eklund and Karlsson (2005) compare the method of Jacobson and Karlsson with the approach of Stock and Watson based on using the first few principal…”
Large Dimensional Factor Analysis (2008, background): “Results concerning the use of factors in forecasting are discussed in Stock and Watson (2006) , Banerjee et al. (2006), and Giannone et al. (2007).”
Systemic Risks and the Macroeconomy (2010, unknown): “Following Stock and Watson (2006), we do not include GDP growth and the FS indicator in the vector Xt of predictors.”
To Combine Forecasts or to Combine Information? (2010, unknown): “In general, this can be understood through the bias and variance trade-o¤ between large and small models.9 To illustrate the nite sample potential gain of CF over CI more explicitly, we consider a simpli ed 7This is also explained in Stock and Watson (2006) and Bai and Ng (2008b) in di¤erent ways.”
The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve (2008, methodology): “Our FSN–ECM models may be interpreted as a special type of dynamic factor model (see Stock and Watson 2006, p. 524) in which the ordinates (yields) at the knots of the spline are the factors and the factor loadings are determined by the requirement that the signal function (i.e., the “common…”
Comparing Alternative Predictors Based on Large-Panel Factor Models (2012, unknown): “Using a large panel of US macro- economic variables, Stock and Watson (2004a) find that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) find that SW’s method largely outperforms the FHLR’s…”
Ranking and combining multiple predictors without labeled data (2013, unknown): “αi = ψ iηi ð1−ψ iÞð1− ηiÞ ; βi = ψ ið1−ψ iÞ ηið1− ηiÞ : [12]”