This course is designed to give students an understanding of why empirical methods are necessary and to provide them with a working knowledge of basic econometric tools so thatThey can apply such tools to modelling, estimation, and inference in the context of real-world economic problems and data.They can evaluate critically the results and conclusions from others who use basic econometric tools like ordinary least squares.They have a foundation and understanding for further study of econometrics and have an appreciation of the range of more advanced techniques (which may be covered in later courses)

This course consists of lectures, practice sessions (including applications to economic data sets using the statistical software R), and a mandatory online tutorial (Datacamp course). Further details can be found in the associated Moodle course.On Practice Session days, the following schedule applies:Group 607.172    15:00-16:30Group 607.182    16:30-18:00Furthermore, we plan to offer a tutorial (course no. 607.272). The tutorial provides a further opportunity to discuss the material presented in the lectures and the practice sessions and is thus very helpful to support the work on and understanding of the exercise sheets (in particular also with regard to the mathematical and statistical foundations).

The Nature of Econometrics and Economic DataThe Simple Regression ModelMultiple Regression Analysis: Estimation and InferenceMultiple Regression Analysis: Specification Analysis and HeteroskedasticitySimple Panel Data ModelsEndogenous RegressorsThe contents discussed may go beyond the material indicated above, i.e., additional material may be provided. Only the chapters and material actually discussed during the semester are relevant for the exam.Preparing for the lectures by, e.g., reading the relevant chapters of the textbook prior to the corresponding teaching units has proven very effective for a positive and successful learning experience.

Sufficient prior economics knowledge as well as a profound understanding of (and the ability to readily apply) the concepts taught in Methodology I: Mathematics and Methodology II: Statistics.

J.M. Wooldridge (2019), Introductory Econometrics. A Modern Approach, 7th edition, Cengage Learning.R.C. Hill, W.E. Griffiths, and G.C. Lim (2018), Principles of Econometrics, 5th edition, Wiley.

1. Practice sessions (max. 24 points): The practice sessions start in the week following the first lecture.For the practice sessions you will have to solve the four exercises of the corresponding exercise sheet as of the first session. Select (tick) the exercises you actually did solve in the checklist in the Campus System and upload the solutions to Moodle by 12:00 on the Tuesday before the practice session.During the practice sessions you will be asked – chosen randomly – to present your solution for any of the exercises you ticked. You are expected to give a short and succinct presentation of your solutions.A necessary condition for successfully completing the practice sessions is to tick at least 50% of the exercises (= 12 out of 24). Every ticked exercise is worth 1 point – the maximum number of points achievable for ticked exercises is therefore 24 points.If it turns out that you are absent (without excuse) or haven’t prepared the exercise, you may be penalized with up to 3 minus points. In addition, you will lose the point for the exercise you ticked in the list (or all if you are absent). Serious flaws in the solutions presented will lead to deductions. You may, however, also be awarded bonus points for particularly good explanations of exercises and contributions (to the discussions). 2. Datacamp R tutorial (max. 6 points): The tutorial must be completed by November 30. The number of XP achieved in the tutorial will be converted 1:1 to the awardable points. 3. Midterm exam (max. 30 points) and final exam (max. 40 points):  Both exams consist of two parts: Multiple choice questions and exercises / questions similar in spirit and complexity to the weekly exercises. Duration: 90 minutes each.  Closed-book exams (= no documents allowed); you may use a non-programmable calculator (and bringing a ruler is also a good idea) Dates:  21 November (midterm exam) and 30 January (final exam)

The exam is based on:the actual contents discussed in the lectures (the list above is, therefore, purely indicative)the material discussed in the practice sessions, both related to the exercise sheets and to potentially discussed further issues.

50% of the achievable points (= 50 out of 100) are required for successful completion of the course. Note that is not necessary to successfully complete each part individually.