Graduate courses in Statistics, Econometrics I and Econometrics II (Master in Economic Analysis)

The goal of this course is twofold. The first half of the syllabus is devoted to get graduate students familiar with a wide range of econometric methods for estimation of SVAR, DSGE and SAM macro models. Special emphasis is placed on the analysis of the transmission and propagation effects on financial, fiscal, monetary and productivity shocks on macro aggregates in product and labour markets. The second half of the course is devoted to estimation and inference in large datasets, including methods like quantile regressions, factor models and machine learning technique. The course will be organized around lectures and paper presentations to provide solid economic theory and econometric background for each topic. The lectures will be complemented with problem sets, that include both theoretical and empirical exercises.

Topic 1. VAR Models in Macro (Brief overview) 1.1 General Framework (VAR, ECM, SVAR) 1.2 Estimation of VAR, ECM, SVAR & Proxy SVAR Models 1.3 Identifying Restrictions: Short & Long-Run, Sign, Heteroskedasticity, IV 1.4 Specifying the Co-integrating Rank 1.5 Bayesian VARs Topic 2. Empirical Applications 2.1 Inflation Shocks and Money Neutrality 2.2 Labour Market Shocks 2.3 Fiscal Shocks 2.4 Identifying Monetary Policy Switching Regimes 2.5 What Do VARs mean when Shocks are Persistent? 2.5. Stock Prices, News Shocks & the Business Cycle 2.6 An Attack on RBC Models: Technology vs. Demand Shocks 2.7 Using DSGE Models to Check Identification in SVARs Topic 3. Miscellanea 3.1 Structural Breaks 3.2 Modelling TS with Changes in Regime through Markov Chains 3.3 Marked-Point Processes in High-frequency data 3.4 Estimation of Taylor Rules & NK Phillips Curves 3.5 Quantile Regression Models 3.6 Large Dimensional Factor Models (Estimation, Forecasting, Breaks, Quantiles) 3.7 Testing for Rational Bubbles. 3.8 Calibration /Estimation of Search & Matching Models Topic 4. Machine Learning Techniques 4.1 Estimating Prediction Error 4.2 Shrinkage and LASSO methods 4.3 Nonlinear methods 4.5 Regression Trees, Random Forests 4.6 Causal Inference with Machine Learning

State-of- art calibration, simulation and estimation techniques.