arXiv 12 Feb 2024 · Econometrics
arXiv:2402.07743 · PDF · DOI · OpenAlex · Extracted main text
This paper presents a comprehensive local projections (LP) framework for estimating future responses to current shocks, robust to high-dimensional controls without relying on sparsity assumptions. The approach is applicable to various settings, including impulse response analysis and difference-in-differences (DiD) estimation. While methods like LASSO exist, they often assume most parameters are exactly zero, limiting their effectiveness in dense data generation processes. I propose a novel technique incorporating high-dimensional covariates in local projections using the Orthogonal Greedy Algorithm with a high-dimensional AIC (OGA+HDAIC) model selection method. This approach offers robustness in both sparse and dense scenarios, improved interpretability, and more reliable causal inference in local projections. Simulation studies show superior performance in dense and persistent scenarios compared to conventional LP and LASSO-based approaches. In an empirical application to Acemoglu, Naidu, Restrepo, and Robinson (2019), I demonstrate efficiency gains and robustness to a large set of controls. Additionally, I examine the effect of subjective beliefs on economic aggregates, demonstrating robustness to various model specifications. A novel state-dependent analysis reveals that inflation behaves more in line with rational expectations in good states, but exhibits more subjective, pessimistic dynamics in bad states.
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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 | Adamek, R., S. Smeekes, and I. Wilms (2024) Local projection inference in high dimensions | 1.000 | 7 | 4 | 100% |
| 2 | Plagborg-Mller, M. and C. K. Wolf (2021) Local projections and VARs estimate the same impulse responses | 0.941 | 6 | 3 | 83% |
| 3 | Belloni, A., V. Chernozhukov, and C. Hansen (2013) Inference on Treatment Effects after Selection among High-Dimensional Controls†| 0.928 | 5 | 5 | 80% |
| 4 | Acemoglu, D., S. Naidu, P. Restrepo, and J. A. Robinson (2019) Democracy does cause growth | 0.928 | 4 | 3 | 100% |
| 5 | Bhandari, A., J. Borovicka, and P. Ho (2024) Survey data and subjective beliefs in business cycle models | 0.874 | 9 | 2 | 100% |
| 6 | Jiang, W (2009) On Uniform Deviations of General Empirical Risks with Unboundedness, Dependence, and High Dimensionality | 0.843 | 4 | 4 | 75% |
| 7 | Adamek, R., S. Smeekes, and I. Wilms (2023) Lasso inference for high-dimensional time series | 0.843 | 4 | 3 | 75% |
| 8 | Montiel Olea, J. L. and M. Plagborg-Mller (2021) Local Projection Inference Is Simpler and More Robust Than You Think | 0.843 | 3 | 3 | 100% |
| 9 | Dube, A., D. Girardi, O. Jorda, and A. M. Taylor (2023) A local projections approach to difference-in-differences event studies, Tech | 0.843 | 3 | 3 | 100% |
| 10 | Ing, C.-K (2020) Model selection for high-dimensional linear regression with dependent observations | 0.806 | 21 | 6 | 52% |
Showing the top 10 of 38 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2602.10415 | 0.737 | 3 | 2 |