Research Areas:

- Theoretical Econometrics: Development of new estimation and inference methods for structural models, time series, panel data, and high-dimensional data.  

- Applied Econometrics: Empirical applications in labor economics, finance, development, industrial organization, and macroeconomics.  

- Machine Learning & Big Data: Integration of machine learning techniques with traditional econometric approaches for causal inference and prediction.  

- Bayesian Econometrics: Bayesian methods for model uncertainty, hierarchical modeling, and decision theory.  

- Spatial & Network Econometrics: Analysis of economic interactions in networked and geographically linked data.  

Teaching:

Our department offers rigorous training in econometric theory and applied data analysis at undergraduate, master's, and PhD levels. Courses cover foundational topics such as linear regression, maximum likelihood estimation, GMM, and nonparametric methods, as well as advanced seminars on structural modeling, program evaluation, and computational econometrics.  

Impact:  

We work closely with policymakers, central banks, and international organizations to address real-world economic challenges. Our research has been published in leading journals such as Econometrica, Journal of Econometrics, and Review of Economic Studies.