Generalized Shrinkage Methods for Forecasting Using Many Predictors

By James H. Stock, Mark W. Watson. 2012

311 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Global and Planetary Change, Management Science and Operations Research

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Citations in the corpus

Sufficient Forecasting Using Factor Models (2015, methodology): “Our dataset is taken from Stock and Watson (2012), which consists of quarterly observations on 108 U.S. low-level disaggregated macroeconomic time series from 1959:I to 2008:IV.”
Systemic Risk and the Macroeconomy: An Empirical Evaluation (2016, background): “This strips out variation in the target variable that is forecastable using its own history and is standard in much of the forecasting literature (e.g., Bai and Ng 2008, Stock and Watson 2012).”
Hierarchical shrinkage priors for dynamic regressions with many predictors (2011, unknown): “If correlation among the predictors is a crucial determinant of the performance of these algorithms, then a natural question to ask is what happens if we forecast with orthogonal predictors (the case that Stock and Watson, 2011, examine).”
Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, methodology): “Forecasts based on large a Following Stock and Watson (2012a) and as discussed in Section 6.1, the trends in the growth rates were estimated using a biweight filter with a bandwidth of 100 quarters; the displayed series subtract off these trends. b For example, McCracken and Ng (2015) have compiled ”
Forecasting: theory and practice (2020, unknown): “This can be achieved through selection (such as Autometrics, Doornik, 2018), shrinkage (including ridge regression: Hoerl and Kennard, 1970; Lasso: Tibshirani, 1996; Zou, 2006; and Elastic Nets: Zou and Hastie, 2005), variable combination (such as Principal Components Analysis and Partial Least Squa”
A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, unknown): “13 We follow Stock and Watson (2012) and Andreou et al. (2013) and extract two common factors (z τ ) from the 132 macroeconomic series using principal components.”
Variable Selection in Predictive Regressions (2013, methodology): “Stock and Watson (2009) use shrinkage as the unifying framework to discuss various forecast methods.”
DYNAMIC FACTOR MODELS (2018, background): “Stock and Watson (2009c) compare factor forecasts to other high-dimensional forecasts in U.S. data and also find mixed results.”