Advanced Econometrics

Catalog description

The course presents advanced econometric methods for cross-sectional, time series and panel data. It introduces estimation methods such as Maximum Likelihood and Generalized Method of Moments for univariate and multivariate linear and nonlinear micro-econometric models, including discrete choice, censored regression and sample selection models. Attention is next turned to time series models, such as stationary ARMA and autoregressive distributed lag models with dynamic causal effects, and issues that arise when nonstationarity is present, such as structural breaks, trends, unit roots and cointegration. The course proceeds to introduce static and dynamic panel data models along with appropriate methodology such as fixed and random effects. It finally considers methods for high-dimensional ("big") data, such as regularization, principal component and factor analysis, and offers an introduction to non-parametric estimation. The students will apply the methods to real data using appropriate econometric packages such as STATA and R.