Learning Objectives
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After the course, students must be able to:
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demonstrate knowledge of the concepts, models, methods and tools of microeconometrics as discussed during the course (when to apply what and why)
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read and understand international research papers that employ microeconometric methods
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perform an econometric analysis on a microeconomic data set in practice. (This implies being able to identify the problem, formulate the theoretical background for solving the problem, specify a suitable econometric model, estimate the model taking any problems into account, perform relevant hypothesis testing, apply the model for the purpose it was constructed for and report the results of the analysis taking the type of reader into account)
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evaluate an empirical study conducted by another person/researcher.
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Examination
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Microeconometrics
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Marking Scale
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7-step scale
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Censorship
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External examiners
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Exam Period
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May/June
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Examination
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Broadly speaking, the goal of the course is to provide students with a toolbox of estimation techniques that will allow them to work with both cross-sectional and panel data. The oral exam will explore the mastery of these tools, both in an applied and theoretical setting.
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Prerequisites for Attending the Exam
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Oral exam based on a group term paper of max- 15 pages written in groups up to 5 students.
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Course Content
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The course consists of three parts. First, an introductory part that sets the stage for the remaining parts. Here concepts such as causality, conditional expectation and some necessary asymptotic theory will be discussed. Secondly, we discuss the use of single equation regression models with an emphasis on endogeneity problems and instrumental variable estimation. This discussion is mainly based on cross-sectional data set.Finally, we focus on more advanced topics: The analysis of panel data sets – their merits, special problems, estimation methods etc. is one topic within this part of the course. Another topic relates to sample selection problems and in the end focus is placed on discrete response models. The course builds on a standard introductory course in econometrics. A good understanding of mathematics and statistics is an advantage. The students need to be familiar with matrix notation. |
Teaching Methods
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Lectures and computer based exercise classes.
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