This paper studies  regression models with lasso when data is sampled under multi-way clustering. First, we establish the convergence rates for the lasso and post-lasso estimators. Second, we propose a novel inference method based on a post-double-selection procedure and show its asymptotic validity. Our procedure can be easily implemented with existing statistical packages. Simulation results demonstrate that the proposed procedure works well in finite sample  .
This paper studies high-dimensional regression models with lasso when data is sampled under multi-way clustering. First, we establish convergence rates for the lasso and post-lasso estimators. Second, we propose a novel inference method based on a post-double-selection procedure and show its asymptotic validity. Our procedure can be easily implemented with existing statistical packages. Simulation results demonstrate that the proposed procedure works well in finite sample . We illustrate the proposed method with a couple of empirical applications to development and growth economics .
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high-dimensional
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the
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. We illustrate the proposed method with a couple of empirical applications to development and growth economics