Yuya Sasaki
Brian and Charlotte Grove Chair
& Professor of Economics
Department of Economics
＆ Data Science Institute
Vanderbilt University
Research Field: Econometrics
yuya.sasaki[at]vanderbilt.edu
● NEWS about my upcoming presentations/discussions/lectures
Seminars at Emory (September 2023), Brandeis (September 2023), Rutgers (October 2023), Ohio State (October 2023), UC Santa Barbara (November 2023), Indiana, (March 2024), Cambridge (May 2024), Oxford (May 2024), Warwick (May 2024), Shandong (May 2024), and Xiamen (May 2024).
Invited Speaker: 2023 Stata Economics Virtual Symposium, November 9, 2023 (Slides)
Invited Lecture on "Clustering" (3 Hours): Institute of Economics, Academia Sinica, November 30, 2023 (Slides)
Panel Discussant: 2024 ASSA Journal of Business & Economic Statistics Session, and Econometrica Session, January 6, 2024
● Frequently Asked Questions about the Stata Command "robustate":
Q1. How does the "robustate" command compare with the existing IPW estimator such as the "teffects ipw" command?
A. "teffects ipw" tends to produce larger standard errors than "robustate". If the overlap is severely limited (i.e., if the tail index of the inverse propensity score is above 0.5), then the standard error for "teffects ipw" is not guaranteed to exist while that of "robustate" still exists.
Q2. How does the "robustate" command compare with the IPW estimation with trimming/truncating small propensity scores?
A. Trimmed and truncated estimators are biased for the average treatment effects (ATE), while the "robustate" estimator is de-biased and its standard error accounts for the effects of the de-biasing.
Q3. How does the "robustate" command compare with the matching estimators such as "teffects pamatch" and "teffects nnmatch" commands?
A. The matching estimators tend to be biased for the average treatment effects (ATE) when the overlap is limited, while the "robustate" estimator being de-biased consistently estimates the ATE and its standard error accounts for the effects of the de-biasing.
Q4. How does the "robustate" command compare with the overlap weighting approaches?
A. The "robustate" estimates the average treatment effects (ATE), while the overlap weighting approaches estimate only weighted averages of treatment effects and hence in general fail to estimate the ATE.
Stata Command:
testout
Diagnostic testing of outliers. Use this command to check if your estimates and standard errors are credible in regress and ivregress.
Stata Command:
robustate
Estimation of the average treatment effects (ATE) robustly against the limited overlap or a weak satisfaction of the common support condition.