Master

Estimation and Inference in Econometrics

Interactive Lecture

The objective of this course is to prepare students for basic and advanced empirical work in economics. After a broad review of probability theory, inferential statistics and basic econometrics, the course focuses on methods for causal inference in econometrics. Successful completion of this course should allow students to read and understand much of the professional empirical literature in economics and related fields. The course also provides hands-on experience in econometric analysis designed to help students to acquire the skills necessary to carry out their own empirical research.

Time Series Econometrics

Interactive Lecture

This lecture provides an introduction to modern time series analysis. It covers basic concepts, univariate stationary processes, estimation, testing and forecasting, univariate nonstationary processes, spurious regressions, unit root tests, multivariate stationary processes, impulse response and variance decomposition analyses, Granger causality, multivariate nonstationary processes, cointegration and vector error correction models.

The Fiscal Theory of the Price Level

Interactive Lecture

Between 2015 and 2021, we observed persistently low levels of inflation despite extremely expansionist monetary policies in many advanced economies. This challenges our understanding on how the price level is determined. A non-orthodox attempt to explain price level and inflation is the fiscal theory of the price level (FTPL), which has developed over the last 25 years. The lecture presents a state-of-the-art exposition of the FTPL.

Empirical Business Cycle Analysis

Interactive Lecture

This course provides an introduction to empirical business cycle analysis using descriptive and structural models from time series econometrics. No prior knowledge of time series econometrics is required, but students should be familiar with basic econometrics. The course surveys the development of business cycles theory in recent decades and shows how theories can be tested empirically using modern time series methods. The lecture focuses on the application of methods, it does not cover the underlying estimation theory. In class, we will replicate the results of severeal seminal papers. In the interactive part of the lecture, students are asked to double-check previous results in the literature in the light of newly available data.