Viet Hoang Dinh, Didier Nibbering, Benjamin Wong
arXiv 3 Jun 2024 · Econometrics · publishedThe Review of Economics and Statistics (2024) · 1 citations (OpenAlex)
arXiv:2406.01002 · PDF · DOI · OpenAlex · Extracted main text
We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response functions across Monte Carlo experiments representative of different macroeconomic settings and identification schemes. (ii) Our results suggest that random subspace methods are more accurate than other dimension reduction methods if the underlying large dataset has a factor structure similar to typical macroeconomic datasets such as FRED-MD. (iii) Our approach leads to differences in the estimated impulse response functions relative to benchmark methods when applied to two widely studied empirical applications.
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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 | Plagborg-Mller, Mikkel and Christian K Wolf (2021) Local projections and VARs estimate the same impulse responses | 0.956 | 8 | 6 | 88% |
| 2 | Forni, Mario and Luca Gambetti (2014) Sufficient information in structural VARs | 0.941 | 6 | 3 | 83% |
| 3 | Ferreira, Leonardo N, Silvia Miranda-Agrippino, and Giovanni Ricco.… Bayesian local projections | 0.928 | 5 | 4 | 80% |
| 4 | Stock, James H and Mark W Watson (2018) Identification and estimation of dynamic causal effects in macroeconomics using external instruments | 0.928 | 4 | 3 | 100% |
| 5 | Leeper, Eric M, Todd B Walker, and Shu-Chun Susan Yang (2013) Fiscal foresight and information flows | 0.874 | 6 | 2 | 100% |
| 6 | Gertler, Mark and Peter Karadi (2015) Monetary policy surprises, credit costs, and economic activity | 0.843 | 4 | 3 | 75% |
| 7 | Stock, James H and Mark W Watson (2016) Dynamic factor models, factor-augmented vector autoregressions, and structural sector autoregressions in macroeconomics | 0.737 | 5 | 3 | 40% |
| 8 | Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin (2010) Large Bayesian vector auto regressions | 0.737 | 3 | 3 | 67% |
| 9 | Boot, Tom and Didier Nibbering (2019) Forecasting using random subspace methods | 0.737 | 3 | 3 | 67% |
| 10 | Buckland, Steven T, Kenneth P Burnham, and Nicole H Augustin (1997) Model selection: An integral part of inference | 0.644 | 3 | 2 | 67% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Local Projections Inference with High-dimensional Covariates without Sparsity | 0.405 | 1 | 1 |