Applied Econometrics for Researchers
CBS PhD School
Prerequisites
The course requires that students have basic knowledge of statistics. It is assumed that students know how to calculate and interpret e.g. mean values, standard deviations and correlations, and how to perform basic statistical tests, e.g. comparing means. Knowledge of any particular programming language is not required although some programming experience will be an advantage. Stata will be the statistical software used and all students will be given access to it during the course.
Aim
The overall aim of the course is to provide econometric tools for data analysis to PhD students with limited prior econometric experience. Students will be able to identify the appropriate econometric technique given their research question and the available data. Students will be able to distinguish between different econometric models and understand the limitations and pitfalls of each tool taught.
Course content
The student will be equipped with tools ranging from Ordinary Least Square to Limited Dependent Variables Models and Count Models useful for cross sectional settings. In this context, students will learn how to handle sample selection bias and endogeneity problems. Furthermore, the student will be exposed to panel data estimation and tools for policy evaluation.
Teaching style
Lectures, workshops, home exercises, student presentations of home exercises.
Lecture plan
Lectures (in-class): Wednesdays 9.00-12.00
Workshops (on-line): Fridays 9.00-12.00
Tuesday 01/10-2024: Introduction to econometrics and Stata essentials (HCK/VR/JGC)
Room: KL2.53 Note: This will be 15.00-18.00, followed by a welcome dinner.
Wednesday 09/10-2024: Lecture: Ordinary Least Squares (HCK)
Room: KL2.53
Friday 11/10-2024: Workshop – Stata essentials (JGC)
Note: Fall break in Week 42
Wednesday 23/10-2024: Lecture: OLS, Dummy Variables and Moderation Effects (HCK)
Room: KL2.53
Friday 25/10-2024: Workshop – Application and interpretation of OLS (JGC)
Wednesday 30/10-2024: Lecture: Limited-dependent Variable Models (HCK)
Room: KL2.53
Friday 01/11-2024: Workshop – Logit/probit models (JGC)
Wednesday 06/11-2024: Lecture: Attrition and Selection Models (VR)
Room: KL2.53
Friday 08/11-2024: Workshop - Selection and Attrition (JGC)
Wednesday 13/11-2024: Lecture: Matching Methods (VR)
Room: KL2.53
Friday 15/11-2024: Workshop - Matching (NN)
Wednesday 20/11-2024: Lecture: Instrumental Variables (VR)
Room: KL2.53
Friday 22/11-2024: Workshop - Instrumental Variables (JGC)
Wednesday 27/11-2024: Lecture: Panel Data Models (VR)
Room: KL2.53
Friday 29/11-2024: Workshop - Panel Data (JGC)
Wednesday 04/12-2024: Lecture: Policy evaluation methods (HCK/VR) Room: KL2.53
Thursday 05/12-2024: Workshop – Policy evaluation (JGC)
Wednesday 11/12-2024: Presentations workshop (Note: in class): (HCK/VR/JGC)Room: KL2.53
Learning objectives
Subsequent attending this course, the student should feel substantially better equipped to tackle econometric challenges, conduct rigorous econometric studies, and to discuss and comment on econometric work of others.
Evaluation
Students are expected to follow the entire program of lectures and workshops and to develop a final assignment report. The assignment must be based on a replication of a published paper, preferably based within each student’s area of interest, using econometric methods taught in the course. Students will receive guidance in locating papers and associated data for the assignment. A 1-page description specifying key variables for the analysis and the research question addressed will be due by November 15. Students should prepare a 15-minute presentation for the class on December 11. The final assignment is a max. 15 pages report that conducts the empirical analysis, presents the results, and critically discusses the methods and assumptions employed by the empirical analysis. The final assignment report is due on December 18.
Note: In case we receive more registrations for the course than we have seats, CBS PhD students will have first priority. Remaining seats will be filled on a first come first serve.
Course Literature (subject to change)
• Ai C., & Norton E.C., (2003). Interaction terms in logit and probit models. Economics Letters, 80 123-129.
• Cameron, A. and Trivedi, P., 2005: Microeconometrics: Methods and Applications, Cambridge University Press (selected chapters).
• Certo, S., Busenbark, J., Woo, H., Semadeni, M, 2015. Sample selection bias and Heckman models in strategic management research, Strategic Management Journal 37 (13) 2639-2657.
• Hoetker G., (2007), The use of logit and probit models in strategic management research: critical issues. Strategic Management Journal, 28 331-343.
• Norton E.C., H. Wang, & Ai C., (2004). Computing interaction effects and standard errors in logit and probit models. The Stata Journal, 4(2) 154-167.
• Wooldridge, J. M. (2015), Introductory Econometrics - A Modern Approach, International Student Edition, 6th Edition, South Western (selected chapters).
• Wooldridge, J. M. (2002), Econometric Analysis of Cross Section and Panel Data, The MIT Press, Cambridge MA (selected chapters).
Note: In case we receive more registrations for the course than we have seats, CBS PhD students will have first priority. Remaining seats will be filled on a first come first serve. The course is offered annually.