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PDSLASSO: Stata module for post-selection and post-regularization OLS or IV estimation and inference

Author

Listed:
  • Achim Ahrens

    (Economic and Social Research Institute, Republic of Ireland)

  • Christian B. Hansen

    (University of Chicago)

  • Mark E Schaffer

    (Heriot-Watt University)

Programming Language

Stata

Abstract

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" (PDS) methodology of Belloni et al. (2012, 2013, 2014, 2015, 2016). (2) The "post-regularization" (CHS) 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.

Suggested Citation

  • Achim Ahrens & Christian B. Hansen & Mark E Schaffer, 2018. "PDSLASSO: Stata module for post-selection and post-regularization OLS or IV estimation and inference," Statistical Software Components S458459, Boston College Department of Economics, revised 24 Jan 2019.
  • Handle: RePEc:boc:bocode:s458459
    Note: This module should be installed from within Stata by typing "ssc install pdslasso". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/p/pdslasso.ado
    File Function: program code
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/p/pdslasso.sthlp
    File Function: help file
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/i/ivlasso.ado
    File Function: program code
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/i/ivlasso_p.ado
    File Function: program code
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/i/ivlasso.sthlp
    File Function: help file
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/i/ivlasso.ihlp
    File Function: help file
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/c/cs_pdslasso.do
    File Function: certification script
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/c/cs_ivlasso.smcl
    File Function: certification script output
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/b/BLP.dta
    File Function: sample data file
    Download Restriction: no
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    Citations

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    Cited by:

    1. Achim Ahrens & Sean Lyons, 2021. "Do rising rents lead to longer commutes? A gravity model of commuting flows in Ireland," Urban Studies, Urban Studies Journal Limited, vol. 58(2), pages 264-279, February.
    2. Simon Büchler, Maximilian v. Ehrlich, 2021. "Quantifying Land Use Regulation and its Determinants - Ease of Residential Development across Swiss Municipalities," Diskussionsschriften credresearchpaper32, Universitaet Bern, Departement Volkswirtschaft - CRED.
    3. Agan, Amanda & Doleac, Jennifer & Harvey, Anna, 2021. "Misdemeanor Prosecution," IZA Discussion Papers 14234, Institute of Labor Economics (IZA).
    4. Jessica Goldberg & Mario Macis & Pradeep Chintagunta, 2018. "Incentivized Peer Referrals for Tuberculosis Screening: Evidence from India," NBER Working Papers 25279, National Bureau of Economic Research, Inc.
    5. Ranveig Falch, 2021. "How Do People Trade Off Resources Between Quick and Slow Learners?," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2021_04, Max Planck Institute for Research on Collective Goods.
    6. Tomaso Duso & Claus Michelsen & Maximilian Schäfer & Kevin Ducbao Tran, 2021. "Airbnb and Rental Markets: Evidence from Berlin," Bristol Economics Discussion Papers 21/746, School of Economics, University of Bristol, UK.
    7. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
    8. Peter Eibich, 2021. "Care or self-care? The impact of informal care provision on health behaviour," MPIDR Working Papers WP-2021-005, Max Planck Institute for Demographic Research, Rostock, Germany.
    9. Laura Derksen & Jason. T Kerwin & Natalia Ordaz Reynoso & Olivier Sterck, 2021. "Appointments: A More Effective Commitment Device for Health Behaviors," CSAE Working Paper Series 2021-13, Centre for the Study of African Economies, University of Oxford.
    10. Do,Quy-Toan & Jacoby,Hanan G., 2020. "Sophisticated Policy with Naive Agents : Habit Formation and Piped Water in Vietnam," Policy Research Working Paper Series 9207, The World Bank.
    11. Felipe González & Magdalena Larreboure, 2021. "The Impact of the Women’s March on the U.S. House Election," Documentos de Trabajo 560, Instituto de Economia. Pontificia Universidad Católica de Chile..
    12. Michael Danquah & Solomon Owusu, 2021. "Digital technology and productivity of informal enterprises: Empirical evidence from Nigeria," WIDER Working Paper Series wp-2021-114, World Institute for Development Economic Research (UNU-WIDER).

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