DRE 7006 Panel Data/ Microeconometrics
DRE 7006 Panel Data/ Microeconometrics
This is an advanced econometric course on the analysis of microeconometric data. The course covers the econometric theory that underpins ordinary least squares and instrumental variable estimation in the context of cross-sectional data and panel data. The course also covers a series of extensions and applications of these techniques that are routinely used in applied microeconometrics with a leaning towards techniques that are used for estimation of causal relationships.
After taking this course, students should
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know how we prove consistency and asymptotic normality of OLS and IV estimators and how we can assess standard errors based on formulas from theory or using bootstrapping methods.
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know how we can interpret OLS and IV estimates when we relax the literal assumptions stated in the models (homogeneous effects, linearity).
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know how we can extend the standard approaches of OLS and IV from analyzing the relationship between the mean outcome and other variables to study the relationship between the full outcome distribution and other variables through quantile regressions and related techniques.
After taking this course, students should
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be able to use simple non-parametric estimation techniques like kernel density estimation and scatterplot smoothing
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be able to analyze data using instrumental variable techniques, taking properly into account the problems associated with weak instruments.
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be able to estimate average treatment effects under the assumption of unconfoundedness/selection on observables with a variety of techniques (inverse propensity score weighting, regression adjustment, matching methods).
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be able to estimate treatment effects based on regression discontinuity designs.
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be able to estimate treatment effects based on a difference-in-differences and related designs.
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be able to analyze large-N panel data, using and understanding the strict exogeneity assumption and clustered standard errors.
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be able to estimate models with essential heterogeneity in treatment effects.
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be able to implement all the covered techniques in statistical software, including running small scale simulation studies to assess estimators under different conditions.
After taking this course, students should
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understand the potential outcomes framework
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understand how we use econometric techniques to try to isolate causal effects from observational data with quasi-experimental features.
Enrollment in a PhD Programme is a general requirement for participation in PhD courses at BI Norwegian Business School.
External candidates are kindly asked to attach confirmation of enrollment in a PhD programme when signing up for a course. Other candidates may be allowed to sit in on courses by approval of the courseleader. Sitting in on a course does not permit registration for the course, handing in exams or gaining credits for the course. Course certificates or confirmation letters will not be issued for sitting in on courses.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
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Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 50 Grouping: Individual Duration: 8 Hour(s) Exam code: DRE 70062 Grading scale: Pass/fail Resit: Examination when next scheduled course |
Exam category: School Exam Form of assessment: Written School Exam - pen and paper Exam/hand-in semester: First Semester Weight: 50 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: DRE 70063 Grading scale: Pass/fail Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Student's own work with learning resources | 113 Hour(s) | Autonomous student learning (including exam preparation) |
Teaching | 36 Hour(s) | |
Examination | 11 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.