Teaching

2021-2025 

• Tutorial & PC: Multivariate Time Series Analysis and Forecasting.

  • Stable VAR processes, structural analysis and forecasting based on VAR models, LS and ML estimators, asymptotics, VAR order selection, forecast loss functions, optimal forecasts and factor models.

• Tutorial & PC: Bayesian Econometrics.

  • Basic Bayesian theory, natural conjugate priors, Bayesian estimation of linear regression models, posterior analysis, model comparison, prediction, Monte Carlo integration, numerical methods and Markov chains, Gibbs sampling, Metropolis-Hastings algorithm and Bayesian VARs.

• Tutorial & PC: Time Series Econometrics.

  • Stationary and nonstationary time series processes, asymptotic properties, ADL models, model selection, unit root tests, cointegration and introduction to VAR models. 

• Tutorial: Introductory Course in Statistics.

  • Probability theory, univariate and bivariate random variables, sampling theory, asymptotic theory and hypothesis testing.

• Lecture: Math Camp (Kiel Institute for the World Economy)

  • Essential mathematical tools for advanced study and research in economics: real analysis, calculus, linear algebra, optimization, probability theory, and estimation.

2019-2020 

• Tutorial & PC: Econometric Methods.

  • Causal effects, OLS estimation, asymptotic theory, inference, omitted variables, IV estimation, GLS estimation, SUR model and M-estimation. 

• PC: Univariate Time Series Analysis.

  • Introduction to R, time series decomposition, ARMA processes, linear filters, stationarity and nonstationarity, estimation and forecasting.

• Tutorial: Introductory Course in Econometrics.

  • Linear regression, OLS estimation and basic assumptions, OLS sampling distribution, hypothesis tests and confidence intervals.