ECON-L6210 - Structural econometrics, Lectures, 25.10.2023-7.12.2023

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Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

After completing this course, students will develop an understanding of the identification of structural econometric models in relation to economic and econometric theory. Students will also learn the theory and estimation strategies behind dynamic discrete choice models.

Credits: 5

Schedule: 25.10.2023 - 07.12.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Ciprian Domnisoru

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    This course covers structural estimation methods, with applications primarily in industrial organization, public, behavioral, and labor economics. It is open to any interested students, particularly PhD students who wish to develop their econometric toolbox and use structural estimation in their work.

    Course schedule (lectures):

    1) Toolbox/review: simulation methods, SML, SMM, indirect inference

    2) Estimation of structural models using experimental data from the lab and the field (Lecturer: Erik Wengström)

    3) Estimation of structural models using experimental data from the lab and the field (Lecturer: Erik Wengström)

    4) Static vs. dynamic considerations: review of value functions/ Bellman equations etc.

    5) Heterogeneity and the EM algorithm

    6) Full solution approaches to solving dynamic structural models.

    7) Full solution approaches: examples

    8) Conditional choice probability theory

    9) Conditional choice probability theory (continued)

    10) Conditional choice probability examples

    11) Conditional choice probability examples: Dynamic games and extensions

    12) Student presentations (may extend to scheduled exam time)

Workload

  • valid for whole curriculum period:

    Three homework assignments applying the methods learned in class using the students’ preferred software + 1 presentation of an attempt to replicate a structural paper in the students’ field of interest. Group work on all assignments is encouraged.

DETAILS

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    2023-2023: II autumn

    Maximum number of participants: 15

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