Harold D. Chiang, Joel Rodrigue, Yuya Sasaki
arXiv 30 Mar 2019 · Econometrics · publishedEconometric Theory (2022) · 5 citations (OpenAlex)
arXiv:1904.00211 · PDF · DOI · OpenAlex · Extracted main text
Three-dimensional panel models are widely used in empirical analysis. Researchers use various combinations of fixed effects for three-dimensional panels. When one imposes a parsimonious model and the true model is rich, then it incurs mis-specification biases. When one employs a rich model and the true model is parsimonious, then it incurs larger standard errors than necessary. It is therefore useful for researchers to know correct models. In this light, Lu, Miao, and Su (2018) propose methods of model selection. We advance this literature by proposing a method of post-selection inference for regression parameters. Despite our use of the lasso technique as means of model selection, our assumptions allow for many and even all fixed effects to be nonzero. Simulation studies demonstrate that the proposed method is more precise than under-fitting fixed effect estimators, is more efficient than over-fitting fixed effect estimators, and allows for as accurate inference as the oracle estimator.
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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 | Lu, X., K. Miao, and L. Su (2018) Determination of Different Types of Fixed Effects in Three-Dimensional Panels, Working paper | 1.000 | 7 | 3 | 100% |
| 2 | Kock, A. B (2016) Oracle Inequalities, Variable Selection and Uniform Inference in High-Dimensional Correlated Random Effects Panel Data Models | 0.950 | 7 | 3 | 86% |
| 3 | Kock, A. B. and H. Tang (2019) Uniform Inference in High-Dimensional Dynamic Panel Data Models with Approximately Sparse Fixed Effects | 0.899 | 11 | 4 | 73% |
| 4 | Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen (2012) Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain | 0.822 | 9 | 3 | 56% |
| 5 | Belloni, A., V. Chernozhukov, C. Hansen, and D. Kozbur (2016) Inference in high-dimensional panel models with an application to gun control | 0.644 | 2 | 2 | 100% |
| 6 | Caner, M. and A. B. Kock (2018) Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso | 0.511 | 3 | 2 | 33% |
| 7 | Balazsi, L., L. Matyas, and T. Wansbeek (2017) Fixed Effects Models, in | 0.511 | 2 | 1 | 100% |
| 8 | Head, K. and T. Mayer (2014) Gravity Equations: Workhorse, Toolkit, and Cookbook, in | 0.511 | 2 | 1 | 100% |
| 9 | Mátyás, L (1997) Proper Econometric Specification of the Gravity Model | 0.511 | 2 | 1 | 100% |
| 10 | Baltagi, B. H. and G. Bresson (2017) Modelling Housing Using Multi-dimensional Panel Data, in | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 35 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Uniform Inference in High-Dimensional Threshold Regression Models | 0.843 | 4 | 3 |
| 2 | Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application | 0.405 | 1 | 1 |