This paper provides students with practical experience of applying standard microeconometric techniques to analyse observational data. In particular, topics covered will give students a familiarity with applied cross sectional and panel econometrics, instrumental variables, propensity score matching and regression discontinuity methods with a special focus on the interpretation of results. Throughout the module students will extensively use STATA econometric software. A large component of the paper is based on replicating empirical studies published in top academic economics journals.
About this paper
| Paper title | Microeconometrics |
|---|---|
| Subject | Economics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,196.41 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- ECON 375
- Restriction
- ECON 490, ECON 495
- Contact
- economics@otago.ac.nz
- Teaching staff
- Murat Genc
- Textbooks
The required textbook is Cameron, A. C. and Trivedi, P. K. (2022) Microeconometrics Using Stata. Stata Press, second edition.
Many papers from Stata Journal will be used.
Other recommended textbooks are:
Angrist, J.D. and Pischke, J.-S. (2009) Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
Wooldridge, J.M. (2010) Econometric Analysis of Cross Section and Panel Data (2nd Edition), MIT Press.- Course outline
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Scholarship, Communication, Critical thinking, Information literacy.
View more information about Otago's graduate attributes. - Learning Outcomes
Students who successfully complete this paper should be able to:
- Demonstrate an understanding of contemporary econometric methods used in cross-sectional and panel data analysis and critically assess their use in applications to economic problems
- Apply microeconometric methods, using appropriate computer software, to relevant data in practice and interpret the results obtained
Timetable
This paper provides students with practical experience of applying standard microeconometric techniques to analyse observational data. In particular, topics covered will give students a familiarity with applied cross sectional and panel econometrics, instrumental variables, propensity score matching and regression discontinuity methods with a special focus on the interpretation of results. Throughout the module students will extensively use STATA econometric software. A large component of the paper is based on replicating empirical studies published in top academic economics journals.
About this paper
| Paper title | Microeconometrics |
|---|---|
| Subject | Economics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2025 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Contact
- economics@otago.ac.nz
- Teaching staff
- Murat Genc
- Textbooks
The required textbook is Cameron, A. C. and Trivedi, P. K. (2022) Microeconometrics Using Stata. Stata Press, second edition.
Many papers from Stata Journal will be used.
Other recommended textbooks are:
Angrist, J.D. and Pischke, J.-S. (2009) Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
Wooldridge, J.M. (2010) Econometric Analysis of Cross Section and Panel Data (2nd Edition), MIT Press.- Graduate Attributes Emphasised
- Interdisciplinary perspective, Scholarship, Communication, Critical thinking, Information literacy.
View more information about Otago's graduate attributes. - Learning Outcomes
Students who successfully complete this paper should be able to:
- Demonstrate an understanding of contemporary econometric methods used in cross-sectional and panel data analysis and critically assess their use in applications to economic problems
- Apply microeconometric methods, using appropriate computer software, to relevant data in practice and interpret the results obtained