AN EMPIRICAL COMPARISON OF METHODS FOR FORECASTING USING MANY PREDICTORS

By James H. Stock, Mark W. Watson. 2005

161 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ccc79a2d244d67e77b8db0b71c22b9d5dc7adb83

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Citations in the corpus

Large Vector Auto Regressions (2011, methodology): “…and financial time series for forecasting, impulse response study and structural analysis, Forni et al. (2000), Stock and Watson (2002a), Stock and Watson (2002b), also seen at Forni et al. (2005), Stock and Watson (2005b), Giannone et al. (2005), and Bańbura et al. (2010) for latest advancements.”
Factor models for matrix-valued high-dimensional time series (2016, background): “ panel time series, when one observes a vector or a panel of variables through time, is also a long studied but still active eld (e.g. Tiao and Box, 1981; Tiao and Tsay, 1989; Engle and Kroner, 1995; Stock and Watson, 2004; Lutk epohl, 2005; Tsay, 2014, and others). Such analysis not only Chen’s res”
A Comprehensive Look at Financial Volatility Prediction by Economic Variables (2011, unknown): “Nevertheless, given that this violation is generally considered to be of minor relevance for the forecasting problem, the literature Stock and Watson (2004) and Wright (2009) generally assumes strict exogeneity, which provides an elegant theoretical framework for model averaging. which is centered…”
Forecasting with Many Predictors (2006, background): “1 This discussion draws on Stock and Watson (2004a).”
Large Bayesian vector auto regressions (2010, methodology): “This is the FAVAR method advocated by Bernanke et al. (2005) and discussed by Stock and Watson (2005b).”
Can aggregation explain the persistence of inflation (2009, methodology): “The described iterative procedure is similar to a recent modification of the EM algorithm for the estimation of factor models in the presence idiosyncratic AR components recently proposed by Stock and Watson (2005).”
Forecasting economic time series using targeted predictors (2008, background): “Results in Stock and Watson (2004a) suggest that the weighted principal components, in the spirit of GLS, indeed provide better forecasts than the OLS based principal component estimates.”
Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components? (2008, unknown): “The availability of the algorithms mentioned above, which are computationally feasible, makes the double-exponential prior an attractive alternative to other priors used for variable selection and requiring computationally demanding algorithms, such as the one proposed by Fernandez, Ley, and Steel (”
Averaging estimators for autoregressions with a near unit root (2010, background): “Stock and Watson (1999, 2004, 2005) have provided detailed empirical evidence demonstrating the gains in forecast accuracy through forecast combination.”
Large Bayesian VARs (2008, unknown): “…and Reichlin, 2005, 2003; Giannone, Reichlin, and Sala, 2004; Marcellino, Stock, and Watson, 2003; Stock and Watson, 2002a,b) and structural analysis (Stock and Watson, 2005b; Forni, Giannone, Lippi, and Reichlin, 2008; Bernanke, Boivin, and Eliasz, 4 2005; Giannone, Reichlin, and Sala, 2004).”
Bayesian VARs with large panels (2007, methodology): “This is the FAVAR method advocated by Bernanke, Boivin, and Eliasz (2005) and discussed by Stock and Watson (2005b).”
Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts (2008, unknown): “In the other five, we use common business cycle factors estimated as in Stock and Watson (2005) to forecast a selection of the monthly predictands considered by Stock and Watson: growth in payroll employment, growth in industrial production, the change in the unemployment rate, the change in the…”
(Un)Predictability and Macroeconomic Stability (2006, unknown): “This observation leads to a new interpretation of the results in Atkenson and Ohanian (2001), Stock and Watson (2005b) and D’Agostino and Giannone (2005) about the deterioration of the inflation forecasts on the basis of 14 ECB Working Paper Series No 605 April 2006 Table 2: Relative MSFEs across…”