QCM: Stata module to implement quantile control method (QCM) via Random Forest

Guanpeng Yan () and Qiang Chen
Additional contact information
Guanpeng Yan: Shandong University of Finance and Economics
Qiang Chen: Shandong University

Statistical Software Components from Boston College Department of Economics

Abstract: qcm implements quantile control method (QCM) via random forest (Chen, Xiao and Yao, 2023), which provides confidence intervals for treatment effects in panel data with a single treated unit using quantile random forest (Meinshausen, 2006). As a nonparametric ensemble learning, QCM is suitable for high-dimensional data, and robust to heteroskedasticity, autocorrelation and model misspecification. Simulations in Chen, Xiao and Yao (2023) show that for 95% nominal confidence level, the empirical coverage rate can reach above 90% if the number of pretreatment periods is 30 or larger. qcm also supports placebo tests using fake treatment units and/or fake treatment time as alternative methods for inference and robustness check.

Language: Stata
Requires: Stata version 16
Keywords: quantile estimates; random forest; treatment effects (search for similar items in EconPapers)
Date: 2023-11-22, Revised 2025-01-11
Note: This module should be installed from within Stata by typing "ssc install qcm". 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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Downloads: (external link)
http://fmwww.bc.edu/repec/bocode/q/qcm.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/q/qcm.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/l/lqcm.mlib Mata object code
http://fmwww.bc.edu/repec/bocode/g/growth.dta supplementary data file (application/x-stata)
http://fmwww.bc.edu/repec/bocode/c/carbontax.dta supplementary data file (application/x-stata)
http://fmwww.bc.edu/repec/bocode/r/repgermany.dta supplementary data file (application/x-stata)

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