https://studiegids.vu.nl/en/courses/2024-2025/E_MFAE_IEBEBy the end of this course students will have had an introduction to modern econometric techniques, that will enable them to conduct an empirical study on their own. In particular, students will be familiar with econometric methods for cross-sectional and panel data, and apply them to real-world applications in macroeconomics, finance and business.First, a review is given of least squares estimation and testing in the simple linear cross-sectional regression model. We study the classical assumptions, and the consequences arising when these assumptions are not fulfilled. The linear model with multiple regressors is discussed using matrix notation. Furthermore, we cover maximum likelihood estimation, and models that are nonlinear in variables. Finally, an introduction to panel data analysis is given. Throughout the course, the focus lies on developing an intuition for state-of-the-art econometric concepts. A balance is struck between theoretical derivations and empirical applications. Extensive use is made of the programming language Python, both for in-class illustration and for hands-on exercises. Students can develop and practice their programming skills using Jupyter Notebooks that provide instructions and feedback on the quality of the code.Lectures (4h per week) and tutorials (2h per week). The latter are used to discuss theoretical and practical exercises.Final written exam (85%) and practical assignment (15%)Main reference: Stock and Watson (2011), "Introduction to Econometrics", Pearson, 3rd edition or newer. Supplementary literature: Wooldridge (2013), "Introductory Econometrics: A Modern Approach", Cengage Learning, Inc. 4th edition or newer.The course is part of the regular track of the SBE faculty minor "Applied Econometrics: A Big Data Experience for All". It is targeted at students who are currently not enrolled in the Bachelor in Econometrics or a similar study program.Participation in this course is a worthwhile preparation for the remaining courses in the regular track of the Minor "Applied Econometrics: A Big Data Experience for All".This course assumes familiarity with probabilistic concepts such as discrete and continuous random variables, conditional expectations, hypothesis testing and central limit theorems, with the basics of matrix calculus, and with the essentials of regression analysis. This material, excluding matrix calculus, corresponds more or less to chapters 1-5 in the book by Stock and Watson (see literature references) and students are recommended to refresh their memory prior to the first lecture, see also Probability and Statistics: A Concise Review at https://www.k-moussa.com/teaching.All materials (slides, theory exercises, practice exams, etc.) are provided on Canvas.