Macroeconomic Forecasting Using Diffusion Indexes

By James H. Stock, Mark W. Watson. 2002

2,819 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Principal Components and Regularized Estimation of Factor Models (2017, background): “8 Zhou et al. (2010) allows for the presence of small noise W .”
Sufficient Forecasting Using Factor Models (2015, methodology): “…(Fama and French, 1992; Carhart, 1997; Fama and French, 2015; Hou et al., 2015), large-scale multiple testing (Leek and Storey, 2008; Fan et al., 2012), high-dimensional covariance matrix estimation (Fan et al., 2008, 2013), and forecasting using many predictors (Stock and Watson, 2002 a , b ).”
Eigenvalue Ratio Test for the Number of Factors (2013, methodology): “Recent examples are Stock and Watson (1999, 2002a, 2002b) and Bernanke, Boivin, and Eliasz (2004), which propose to forecast major economic variables using the factor estimates from a large number of economic variables.”
Common Factors in Latin America's Business Cycles (2006, positive): “Stock and Watson (1989, 2002) and Forni, Hallin, Lippi and Reichlin (2000) have shown that the application of dynamic common factor models to a sufficiently representative set of macroeconomic and sectorial indicators provides superior forecasting performance for a target variable such as real GDP…”
Dynamic factor model with infinite‐dimensional factor space: Forecasting (2016, methodology): “An alternative method is used in Stock and Watson (2002a).”
A test of cross section dependence for a linear dynamic panel model with regressors (2009, background): “…of unobserved factors and individual-speci c factor loadings that give rise to heterogenous cross section dependence (see Forni and Reichlin, 1998, Robertson and Symons, 2000, Phillips and Sul, 2003, Stock and Watson, 2002, Bai and Ng, 2004, Moon and Perron, 2004, Pesaran, 2006, among others).”
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…”
An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series (2006, methodology): “We provide further support for our findings via examination of the large (215 variable) dataset used in Stock and Watson (2002), and via discussion of a series of Monte Carlo experiments that examine the predictive performance of ARFIMA model.”
Inverse Moment Methods for Sufficient Forecasting Using High-Dimensional Predictors (2016, positive): “For a given target time series, we model the multi-step-ahead variable as: yht+h = g(φ ′ 1ft, ...,φ ′ Lft, h t+h), where yht+h = h −1∑h i=1 yt+i is the variable to forecast, as in Stock and Watson (2002), and L is taken to be 1 or 2.”
A diagnostic criterion for approximate factor structure (2016, methodology): “Appendix 6 Link with Stock and Watson (2002) We consider the EM algorithm proposed by Stock and Watson (2002) applied to residuals ε̂i,t:”
Comparing Alternative Predictors Based on Large-Panel Factor Models (2012, positive): “Our data set, borrowed from Stock and Watson (2002), consists of 146 variables for the US economy.”
To Combine Forecasts or to Combine Information? (2010, methodology): “It is important to recall that the consistent estimation of ̂ requires a large N (Stock and Watson 2002b, Bai 2003).”