arXiv 28 Nov 2016 · Statistics — Methodology · publishedEconometric Theory (2018) · 9 citations (OpenAlex)
arXiv:1611.09420 · PDF · DOI · OpenAlex · Extracted main text
We consider inference about coefficients on a small number of variables of interest in a linear panel data model with additive unobserved individual and time specific effects and a large number of additional time-varying confounding variables. We allow the number of these additional confounding variables to be larger than the sample size, and suppose that, in addition to unrestricted time and individual specific effects, these confounding variables are generated by a small number of common factors and high-dimensional weakly-dependent disturbances. We allow that both the factors and the disturbances are related to the outcome variable and other variables of interest. To make informative inference feasible, we impose that the contribution of the part of the confounding variables not captured by time specific effects, individual specific effects, or the common factors can be captured by a relatively small number of terms whose identities are unknown. Within this framework, we provide a convenient computational algorithm based on factor extraction followed by lasso regression for inference about parameters of interest and show that the resulting procedure has good asymptotic properties. We also provide a simple k-step bootstrap procedure that may be used to construct inferential statements about parameters of interest and prove its asymptotic validity. The proposed bootstrap may be of substantive independent interest outside of the present context as the proposed bootstrap may readily be adapted to other contexts involving inference after lasso variable selection and the proof of its validity requires some new technical arguments. We also provide simulation evidence about performance of our procedure and illustrate its use in two 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 | Belloni, A., Chernozhukov, V., Hansen, C. and Kozbur, D (2015) Inference in high dimensional panel models with an application to gun control self | 1.000 | 15 | 3 | 100% |
| 2 | Belloni, A., Chernozhukov, V., Fernández-Val, I. and Hansen, C (2014) Program evaluation with high-dimensional data self | 0.928 | 4 | 3 | 100% |
| 3 | Ahn, S. and Horenstein, A (2013) Eigenvalue ratio test for the number of factors | 0.874 | 5 | 2 | 100% |
| 4 | Belloni, A., Chernozhukov, V. and Hansen, C (2014) Inference on treatment effects after selection among high-dimensional controls self | 0.855 | 8 | 6 | 62% |
| 5 | Acemoglu, D., Johnson, S. and Robinson, J. A (2001) The colonial origins of comparative development: An empirical investigation | 0.846 | 11 | 2 | 91% |
| 6 | Stock, J. and Watson, M (2002) Forecasting using principal components from a large number of predictors | 0.843 | 3 | 3 | 100% |
| 7 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.843 | 3 | 3 | 100% |
| 8 | Andrews, D. W (2002) Higher-order improvements of a computationally attractive k-step bootstrap for extremum estimators | 0.811 | 4 | 2 | 100% |
| 9 | Dezeure, R., Bühlmann, P. and Zhang, C.-H (2016) High-dimensional simultaneous inference with the bootstrap | 0.811 | 4 | 2 | 100% |
| 10 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.737 | 3 | 2 | 100% |
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