Stata新命令 pdslasso: 众多控制变量和工具变量如何

Source: Github - aahr1 / pdslasso

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Stata package: pdslasso

pdslasso and ivlasso are routines for estimating structural parameters in linear models with many controls and/or instruments. The routines use methods for estimating sparse high-dimensional models, specifically the lasso (Least Absolute Shrinkage and Selection Operator, Tibshirani 1996) and the square-root-lasso (Belloni et al. 2011, 2014).

These estimators are used to select controls (pdslasso) and/or instruments (ivlasso) from a large set of variables (possibly numbering more than the number of observations), in a setting where the researcher is interested in estimating the causal impact of one or more (possibly endogenous) causal variables of interest.

Two approaches are implemented in pdslasso and ivlasso:

1. The post-double-selection methodology of Belloni et al. (2012, 2013, 2014, 2015, 2016).
2. The post-regularization methodology of Chernozhukov, Hansen and Spindler (2015).

For instrumental variable estimation, `ivlasso implements weak-identification-robust hypothesis tests and confidence sets using the Chernozhukov et al. (2013) sup-score test.

The implemention of these methods in pdslasso and ivlasso require the Stata program rlasso (available in the separate Stata module lassopack), which provides lasso and square root-lasso estimation with data-driven penalization.

Installation

To install the latest version from SSC, type

ssc install lassopack, replace
ssc install pdslasso, replace

Help files

For further information on pdslasso and ivlasso, type

help pdslasso

The help files contain more information about the implemented routines and examples.

Acknowledgements

Thanks to Sergio Correia for advice on the use of the FTOOLS package.

Citation

pdslasso and ivlasso are not official Stata commands. They are free contributions to the research community, like a paper.
Please cite it as such:

Ahrens, A., Hansen, C.B., Schaffer, M.E. 2018. pdslasso and ivlasso: Progams for post-selection and post-regularization OLS or IV estimation and inference. http://ideas.repec.org/c/boc/bocode/s458459.html

Authors

Achim Ahrens, Economic and Social Research Institute, Ireland

Christian B. Hansen, University of Chicago, USA

Mark E Schaffer, Heriot-Watt University, UK

Issues and questions

If you have encountered any issues with pdslasso, contact achim.ahrens(at)esri.ie and m.e.schaffer(at)hw.ac.uk. If you have questions about the use of pdslasso, contact us via Statalist.

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