Chapter 10 Forecasting with Many Predictors

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

233 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/998e7af5eb2865f05f951a8d48d509c1ce83236c

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Citations in the corpus

Complete subset regressions (2013, background): “1 See, e.g., Stock and Watson (2006) for a review of the literature.”
Averaging estimators for autoregressions with a near unit root (2010, background): “Some excellent reviews include Granger (1989), Clemen (1989), Diebold and Lopez (1996), Hendry and Clements (2002), Timmermann (2006) and Stock and Watson (2006).”
Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion (2010, background): “Unlike more structural approaches, it avoids having to take a stand on which particular variables agents use to compute their forecasts, a decision that in practice can be very complicated, see Stock and Watson (2002, 2006).”
Systemic Risks and the Macroeconomy (2010, methodology): “Following Stock and Watson (2006), we do not include GDP growth and the FS indicator in the vector Xt of predictors.”
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…”
Asymptotics of the principal components estimator of large factor models with weakly influential factors (2012, background): “Approximate factor models have recently attracted an increasing amount of attention from researchers in macroeconomics and nance (see Reichlin (2003), Stock and Watson (2006) and Bai and Ng (2008) for a survey of numerous applications).”
Forecasting with Medium and Large Bayesian VARs (2013, methodology): “Traditionally, researchers working with so many macroeconomic variables have used factor methods (e.g. Stock and Watson, 2002, 2006, Forni, Hallin, Lippi and Reichlin, 2003, Koop and Potter, 2004 and Korobilis, 2009).”
Variable Selection in Predictive Regressions (2013, background): “Then β̂F (L) = β̂F , β̂F = 1 T F̂′Y = 1√ T U ′X,1:rXY = 1√ T D−1X β̂PC,i. (12) The diffusion index forecast is ŶF = rX∑ j=1 UX,jU ′ X,jY = ŶPC .. (13) A review of factor based forecasts is given in Stock and Watson (2006).”