Get a solid foundation in estimation strategies used in econometric modeling. Learn the fundamentals of OLS, the logic of Maximum Likelihood Estimation, and the robustness of Generalized Method of Moments for real-world applications.

Understand how to model binary, censored, and categorical response variables using advanced regression frameworks such as Logit, Probit, and Tobit models.

Explore regression models tailored for count-dependent variables, often used in health, labor, and policy research. Handle over-dispersion, zero-inflation, and truncation challenges.

Learn to build and estimate multi-equation systems with interdependent variables. Understand the critical concepts of identification and apply Two-Stage and Three-Stage Least Squares methods.

Model the dynamics of time and behavior using lag structures. Learn how past values influence present outcomes and build autoregressive frameworks for economic data.

Combine cross-sectional and time series analysis using panel data techniques. This section trains you on advanced estimation with both fixed and random effects.

Master quasi-experimental techniques using DID models for program and policy evaluation. Use longitudinal data to estimate treatment effects.

Go beyond average effects by modeling different points of the conditional distribution of your outcome variable. Ideal for skewed or outlier-prone data.

Learn when and how to apply nonlinear modeling techniques using flexible functional forms and iterative estimation methods.

Reduce data dimensionality while preserving structure. Learn how to extract uncorrelated principal components from multivariate datasets.