Profile Information

Profile

I am an econometrician with the title of Associate Professor in the John E. Walker Department of Economics at Clemson University. Previously, I was
the Executive Director of Econometrics at Stata, and an Associate Professor
at Sam Houston State University.

I am an active researcher with papers in the American Economic Review,
Econometric Theory, Econometric Reviews, the Journal of Regional Science,
Economics Letters, Econometrics and Statistics, and the Stata Journal; among
other places. I was also the principal investigator on two large research
grants.

My current research interests lie in the areas of high-dimensional models and
inference after model selection. My overall research agenda has been to make
useful, robust econometric methods implementable, accessible, and
understandable by all.

I have a long-standing commitment to education. While at Stata, I gave over
150 short courses or talks to researchers and students at universities,
conferences, and training sites around the world. While at Sam Houston State, I
taught introduction to business statistics, introduction to
econometrics, and introduction Python programming for Data Science to
undergraduates. At Clemson, I will teach courses in graduate
Econometrics.

I earned a Ph.D. in Economics from the University of Texas at Austin. My
passion for programming and econometrics took me to Stata in 1999 where I
remained until January 2020. I had a profound effect on Stata, its user base,
and its perception. I developed many Stata commands. I contributed to Stata
in the areas of high-dimensional models, post-model selection inference,
causal inference, panel data, time-series data, spatial econometrics,
cross-sectional data, and models for endogenous variables. I played a key
role in the initial development of Stata MP (I was the PI for the NIH/SBIR
grant that funded the initial development of Stata MP.) I helped integrate
Mata into Stata, and I helped develop some of Stata's numerical techniques.

Research Interests

• Econometrics, Statistical programming, machine learning

Research Publications

• Drukker, David M., Egger, Peter, and Prucha, Ingmar (2023). Simultaneous Equations Models with Higher-order Spatial or Social Network Interactions. Econometric Theory, 39(6):1144-1201.
• Drukker, David M. 2023. Simultaneous tests and confidence bands for Stata estimation commands. The Stata Journal 23(2):518-544.
• Drukker, David M. and Di Liu. 2023. posw: A command for the stepwise Neyman-orthogonal estimator. The Stata Journal 23(2):402-417.
• Drukker, David M. and Di Liu. 2022. A cluster plugin method for selecting the GLM lasso tuning parameters in models for unbalanced panel data. In press at Econometrics and Statistics.
• Drukker, David M. and Di Liu. 2022. “Finite-sample results for lasso and stepwise Neyman-orthogonal Poisson estimators”. Econometric Reviews 41(9) 1047-1076.
• Drukker, David M. (2020) Lasso and Post-lasso Inference. In P. Atkinson, S. Delamont, A. Cernat, J.W. Sakshaug, & R.A. Williams (Eds.), SAGE Research Methods Foundations.
• Drukker, David M. (2017) “Two-part models are robust to endogenous selection”, Economics Letters, March, 152:71–72.
• Drukker, David M. (2016) “A generalized regression-adjustment estimator for average treatment effects from panel data”. Stata Journal 16(4):826–836.
• Stinchcombe, M. B. and Drukker, D. M. (2013) “Regression Efficacy and the Curse of Dimensionality” in Recent Advances and Future Directions in Causality, Prediction and Specification Analysis: Essays in Honor of Halbert L. White Jr. Springer:New York.
• Drukker, D. M. and Ingmar R. Prucha (2013) “On the I2(q) test statistic for spatial dependence: finite-sample standardization and properties” Spatial Economic Analysis 8(3):271–292.
• Cattaneo, M. D and Drukker, D. M. (2013) “Estimation of Multivalued Treatment Effects under Conditional Independence”, Stata Journal 13(3).
• Drukker, D. M.; Peng, H.; Prucha, I. R.; and Raciborski, R. (2013) “Creating and managing spatial-weighting matrices using the spmat command”, Stata Journal 13(2):242–286.
• Drukker, D. M.; Prucha, I. R.; and Raciborski, R. (2013) “Maximum likelihood and generalized spatial two-stage least-squares estimators for a spatial-autoregressive model with spatial-autoregressive disturbances”, Stata Journal 13(2):221-241.
• Drukker, D. M.; Prucha, I. R.; and Raciborski, R. (2013) “A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables”, Stata Journal 13(2):287–301.
• Drukker, D. M., P. Egger, and I. Prucha (2013) “On Single Equation GMM Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances”, Econometric Reviews 32(1): 686-733.
• Drukker, D. M., and R. B. Gates (2011) “State Space Methods in Stata”, Journal of Statistical Software 41(10):1–25.
• Arraiz, I., and D. Drukker, H. Kelejian and I. Prucha (2010) “A spatial Cliff-Ord-type model with Heteroskedastic innovations: Small and large sample results”, Journal of Regional Science, 50(2):592–614.