Generalized Shrinkage Methods for Forecasting Using Many Predictors
Bibliographic Data
| ID | 19418503 |
|---|---|
| Authors | James H Stock (Harvard University), Mark W Watson (0009-0009-9986-3466, Princeton University) |
| Year | 2012 |
| Volume | 30 |
| Issue | 4 |
| Pages | 481-493 |
| Publication date | 2012-10-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2012.715956 |
| OpenAlex | W2110383584 |
| Language | EN |
| Citations received | 18 |
| References cited | 38 |
This article provides a simple shrinkage representation that describes the operational characteristics of various forecasting methods designed for a large number of orthogonal predictors (such as principal components). These methods include pretest methods, Bayesian model averaging, empirical Bayes, and bagging. We compare empirically forecasts from these methods with dynamic factor model (DFM) forecasts using a U.S. macroeconomic dataset with 143 quarterly variables spanning 1960–2008. For most series, including measures of real economic activity, the shrinkage forecasts are inferior to the DFM forecasts. This article has online supplementary material
Econometrics · Machine learning · Shrinkage · Statistics · Computer Science · Energy Load and Power Forecasting · Grey System Theory Applications · Hydrology and Drought Analysis · Mathematics · Artificial Intelligence
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| Unique citing works | 18 |
|---|---|
| Citations per year | 1,8 |
| Citation span | 2016 - 2026 (11) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 17 |