https://studiegids.vu.nl/en/courses/2024-2025/E_MFAE_TSDEThis course introduces students to time series analysis and dynamic econometric models.This course focuses on the econometric analysis of data that contains a temporal component. To that end, the concept of a time series will be rigorously defined and the most important tools to analyse them are discussed. The students are introduced to well-known regression models for time-series data, including autoregressive moving average (ARMA) models, as well as autoregressive distributed lag (ADL) and error correction models (ECM). The course provides both theoretical and practical insights into parameter estimation for time series models and the use of these models for forecasting, testing for Granger causality, and performing policy analysis using impulse response functions. Finally, students become familiar with the fundamental problem of spurious regression in time series analysis. We therefore consider the theory and practice behind unit-root tests, cointegration tests, as well as error-correction representation theorems.Lectures and practical classes. In the practical classes there will be room for asking questions about the homework and the assignment.Final exam (written) and group assignments.The relevant literature consists of the lecture slides and other material provided by the teacher.The course is part of the technical track of the SBE faculty minor "Applied Econometrics: A Big Data Experience for All". It is targeted at students who are currently enrolled in the Bachelor in Econometrics or a similar study program.This course assumes familiarity with the basics of probability calculus and mathematical statistics, which will be used heavily throughout the course. Students are strongly recommended to review this material before the start of the course; a concise review of probability and statistics can be found in these notes. Some familiarity with limits (analysis) and matrices (linear algebra) is also assumed, hence courses in Analysis and Linear Algebra are also recommended background knowledge. Basic experience with programming is recommended, as the group assignment is made in a programming language of choice (Python / R / Matlab / Ox / other). A brief introduction to Python can be found here. Because this course is part of the technical track of the minor Applied Econometrics, it assumes background knowledge at the level of students who completed the first two years of a Bachelor in Econometrics.