M. Hashem Pesaran, Ron P. Smith
arXiv 26 Jan 2024 · Econometrics · publishedNational Institute Economic Review (2024)
arXiv:2401.14582 · PDF · DOI · OpenAlex · Extracted main text
Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chudik, Pesaran, and Sharifvaghefi (2023) Variable selection in high dimensional linear regressions with parameter instability | 0.737 | 3 | 2 | 100% |
| 2 | Sharifvaghefi (2023) Variable selection in linear regressions with many highly correlated covariates | 0.737 | 3 | 2 | 100% |
| 3 | Zou and Hastie (2005) Regularization and variable selection via the elastic net | 0.644 | 2 | 2 | 100% |
| 4 | Pesaran, Pick, and Pranovich (2013) Optimal forecasts in the presence of structural breaks | 0.644 | 2 | 2 | 100% |
| 5 | Lahiri (2021) Necessary and sufficient conditions for variable selection consistency of the LASSO in high dimensions | 0.511 | 2 | 1 | 100% |
| 6 | Shrader, Bakkensen, and Lemoine (2023) Fatal Errors: The Mortality Value of Accurate Weather Forecasts | 0.405 | 1 | 1 | 100% |
| 7 | Whittle (1983) Prediction and Regulation by Linear Least-Square Methods | 0.405 | 1 | 1 | 100% |
| 8 | Bergmeir, Hyndman, and Koo (2018) A note on the validity of cross-validation for evaluating autoregressive time series prediction | 0.405 | 1 | 1 | 100% |
| 9 | Bernanke, Boivin, and Eliasz (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach | 0.405 | 1 | 1 | 100% |
| 10 | Chudik, Grossman, and Pesaran (2016) A multi-country approach to forecasting output growth using PMIs self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Forecasting Oil Consumption: The Statistical Review of World Energy Meets Machine Learning | 0.405 | 1 | 1 |