Achim Ahrens, Christian B. Hansen, Mark E. Schaffer
arXiv 16 Jan 2019 · Econometrics · publishedThe Stata Journal Promoting communications on statistics and Stata (2020) · 36 citations (OpenAlex)
arXiv:1901.05397 · PDF · DOI · OpenAlex · Extracted main text
This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors $p$ may be large and possibly greater than the number of observations, $n$. We offer three different approaches for selecting the penalization (`tuning') parameters: information criteria (implemented in lasso2), $K$-fold cross-validation and $h$-step ahead rolling cross-validation for cross-section, panel and time-series data (cvlasso), and theory-driven (`rigorous') penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performance of the penalization approaches.
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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 | width30.25006ptheight2.62222ptdepth-2.25222pt (2014) Pivotal estimation via square-root Lasso in nonparametric regression | 1.000 | 6 | 4 | 100% |
| 2 | Belloni, A., V. Chernozhukov, and L. Wang (2011) Square-root lasso: pivotal recovery of sparse signals via conic programming | 1.000 | 5 | 3 | 100% |
| 3 | Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen (2012) Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain | 0.874 | 10 | 2 | 100% |
| 4 | Hastie, T., R. Tibshirani, and J. Friedman (2009) The Elements of Statistical Learning | 0.843 | 3 | 3 | 100% |
| 5 | Arlot, S., and A. Celisse (2010) A survey of cross-validation procedures for model selection | 0.811 | 4 | 2 | 100% |
| 6 | Belloni, A., V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 0.811 | 4 | 2 | 100% |
| 7 | Belloni, A., V. Chernozhukov, C. Hansen, and D. Kozbur (2016) Inference in High Dimensional Panel Models with an Application to Gun Control | 0.737 | 3 | 2 | 100% |
| 8 | Zou, H., T. Hastie, and R. Tibshirani (2007) degrees of freedom | 0.737 | 3 | 2 | 100% |
| 9 | Chen, J., and Z. Chen (2008) Extended Bayesian information criteria for model selection with large model spaces | 0.644 | 4 | 1 | 100% |
| 10 | Chernozhukov, V., C. Hansen, and M. Spindler (2015) Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments | 0.644 | 2 | 2 | 100% |
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