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 30/9-2025: Introduction to econometrics and Stata essentials (HCK/VR/NN) Room: KL2.53                         
Note: This will be 15.00-18.00, followed by a welcome dinner.

Wednesday 08/10-2025: Lecture: Ordinary Least Squares (HCK)
Room: KL2.53 

Friday 10/10-2025: Workshop – Stata essentials (NN) 
Note: Fall break in Week 42

Wednesday 22/10-2025: Lecture: OLS, Dummy Variables and Moderation Effects (HCK)
Room: KL2.53

Friday 24/10-2025: Workshop – Application and interpretation of OLS (NN)

Wednesday 29/10-2025: Lecture: Limited-dependent Variable Models (HCK)
Room: KL2.53

Friday 31/10-2025: Workshop – Logit/probit models (NN)

Wednesday 05/11-2025: Lecture: Attrition and Selection Models (VR) 
Room: KL2.53

Friday 07/11-2025: Workshop - Selection and Attrition (NN)

Wednesday 12/11-2025: Lecture: Matching Methods (VR)
Room: KL2.53

Friday 14/11-2025: Workshop - Matching (NN)

Wednesday 19/11-2025: Lecture: Instrumental Variables (VR)
Room: KL2.53

Friday 21/11-2025: Workshop - Instrumental Variables (NN)

Wednesday 26/11-2025: Lecture: Panel Data Models (VR)
Room: KL2.53

 27/11-2025: Workshop - Panel Data (NN)

Wednesday 03/12-2025: Lecture: Policy evaluation methods (HCK/VR) Room: KL2.53

 Friday 05/12-2025: Workshop – Policy evaluation (NN)

Wednesday 10/12-2025: Presentations workshop (Note: in class): (HCK/VR/NN) 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 14.  Students should prepare a 15-minute presentation for the class on December 10. 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 17.

Maximum number of participants: 12

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).