Zhenzhong Wang, Zhengyuan Zhu, Cindy Yu
arXiv 20 Jul 2020 · Econometrics · publishedEconometrics and Statistics (2023) · 3 citations (OpenAlex)
arXiv:2007.10160 · PDF · DOI · OpenAlex · Extracted main text
In the data-rich environment, using many economic predictors to forecast a few key variables has become a new trend in econometrics. The commonly used approach is factor augment (FA) approach. In this paper, we pursue another direction, variable selection (VS) approach, to handle high-dimensional predictors. VS is an active topic in statistics and computer science. However, it does not receive as much attention as FA in economics. This paper introduces several cutting-edge VS methods to economic forecasting, which includes: (1) classical greedy procedures; (2) l1 regularization; (3) gradient descent with sparsification and (4) meta-heuristic algorithms. Comprehensive simulation studies are conducted to compare their variable selection accuracy and prediction performance under different scenarios. Among the reviewed methods, a meta-heuristic algorithm called sequential Monte Carlo algorithm performs the best. Surprisingly the classical forward selection is comparable to it and better than other more sophisticated algorithms. In addition, we apply these VS methods on economic forecasting and compare with the popular FA approach. It turns out for employment rate and CPI inflation, some VS methods can achieve considerable improvement over FA, and the selected predictors can be well explained by economic theories.
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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 | James H. Stock and Mark W. Watson (2002) Forecasting using principal components from a large number of predictors | 1.000 | 7 | 3 | 100% |
| 2 | Jushan Bai and Serena Ng (2008) Forecasting economic time series using targeted predictors | 1.000 | 5 | 3 | 100% |
| 3 | Michael W. McCracken and Serena Ng (2016) FRED-MD: A monthly database for macroeconomic research | 0.941 | 6 | 3 | 83% |
| 4 | Jin-Chuan Duan (2019) Variable selection with big data based on zero norm and via sequential monte carlo | 0.909 | 8 | 4 | 75% |
| 5 | James H Stock and Mark W Watson (2002) Macroeconomic forecasting using diffusion indexes | 0.737 | 3 | 2 | 100% |
| 6 | James H. Stock and Mark W. Watson (2006) Chapter 10 forecasting with many predictors | 0.644 | 2 | 2 | 100% |
| 7 | Simon Foucart (2011) Hard thresholding pursuit: An algorithm for compressive sensing | 0.511 | 2 | 1 | 100% |
| 8 | Jianqing Fan and Runze Li (2001) Variable selection via nonconcave penalized likelihood and its oracle properties | 0.511 | 2 | 1 | 100% |
| 9 | P. Bühlmann and S. van de Geer (2011) Statistics for High-Dimensional Data: Methods, Theory and Applications | 0.511 | 2 | 1 | 100% |
| 10 | Yundong Tu and Tae-Hwy Lee (2019) Forecasting using supervised factor models | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 39 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2512.02092 | 0.405 | 1 | 1 |