Mark Edwin Schaffer

Mark Edwin Schaffer is a Professor at Heriot-Watt University's School of Social Sciences and Edinburgh Business School. His work bridges applied econometrics and economics of transition, focusing on Central and Eastern Europe, the former Soviet Union, and China.

• Education
  • AB, Magna Cum Laude in Social Studies, Harvard University (1982)
  • MScEcon in Economics, London School of Economics (1984)
  • MA in Economics, Stanford University (1984)
  • PhD in Economics, London School of Economics (1990)

Schaffer's research expertise spans econometric methodology, machine learning applications, and empirical analysis of transition economies. Recent work includes double/debiased machine learning in Stata, stacking regression for time-series forecasting, and fused LASSO in quantile regression. His empirical studies examine wage policies in Russian firms, institutional impacts on growth, and energy demand dynamics.

Key contributions include software modules like ACTEST for autocorrelation testing and AVAR for covariance estimation. His 2025 article on model averaging and machine learning highlights his focus on methodological innovation. Earlier work (1990s-2010s) analyzes capital structure in transition firms, fiscal policy, and labor reallocation during economic crises.

Schaffer's collaborations span institutions like IZA Institute of Labor Economics and Stanford University. He actively engages in invited talks on machine learning applications in economics, including gender wage gap estimation and conformal inference.