The PhD/Doctoral Advanced Econometrics rubric is designed to evaluate students’ mastery of advanced econometric techniques and their ability to apply these methods to complex economic problems. This rubric assesses theoretical understanding; methodological rigor; and practical implementation; ensuring students develop the analytical skills necessary for high-level research. Students will demonstrate proficiency in advanced estimation techniques; including maximum likelihood; generalized method of moments (GMM); and Bayesian econometrics. The rubric emphasizes the ability to derive estimators; prove asymptotic properties; and justify model selection. By meeting these criteria; students strengthen their capacity to contribute original research to the field. A key focus is on handling endogeneity; panel data; and time-series econometrics. Students must show competence in addressing identification challenges; applying instrumental variables; and working with high-dimensional datasets. These skills prepare them for empirical work in academia; policy analysis; or industry settings where robust causal inference is essential. The rubric also evaluates computational proficiency; requiring students to implement models using statistical software such as R; Python; or Stata. This ensures they can translate theoretical knowledge into actionable results; a critical skill for modern econometricians. Finally; the rubric assesses students’ ability to communicate findings clearly; both in writing and presentations. Effective dissemination of complex results is vital for influencing policy and advancing scholarly discourse. By meeting the standards outlined in this rubric; students will emerge as skilled econometricians capable of tackling cutting-edge research questions with precision and innovation. The rigorous training prepares them for careers in research institutions; government agencies; or private sector roles requiring advanced quantitative analysis.
