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Applied Econometrics for Researchers - 6 ECTS
Date and time
Tuesday 3 October 2023 at 09:00 to Thursday 14 December 2023 at 12:00
Registration Deadline
Tuesday 10 October 2023 at 23:55
Location
Kilen - room KL 2.53 (second floor),
Kilevej 14A,
2000 Frederiksberg
Kilen - room KL 2.53 (second floor)
Kilevej 14A
2000 Frederiksberg
Applied Econometrics for Researchers - 6 ECTS
Course coordinator: Hans Christian Kongsted, Department of Strategy and Innovation (SI)
Faculty
Professor Hans Christian Kongsted (HCK)
Department of Strategy and Innovation, CBS
Department of Strategy and Innovation, CBS
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.
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.
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.
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.
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
Lectures (in-class): Wednesdays 9.00-12.00
Workshops (on-line): Fridays 9.00-12.00
Tuesday 03/10-2023: Introduction to econometrics and Stata essentials (HCK/VR/)
Room: KL2.53 Note: This will be 15.00-18.00, followed by a welcome dinner.
Wednesday 11/10-2023: Lecture: Ordinary Least Squares (HCK)
Room: KL2.53
Friday 13/10-2023: Workshop – Stata essentials (LP)
Note: Fall break in Week 42
Note: Fall break in Week 42
Wednesday 25/10-2023: Lecture: OLS, Dummy Variables and Moderation Effects (HCK)
Room: KL2.53
Friday 27/10-2023: Workshop – Application and interpretation of OLS (LP)
Wednesday 01/11-2023: Lecture: Limited-dependent Variable Models (HCK)
Room: KL2.53
Friday 03/11-2023: Workshop – Logit/probit models (LP)
Wednesday 08/11-2023: Lecture: Attrition and Selection Models (VR)
Room: KL2.53
Friday 10/11-2023: Workshop - Selection and Attrition (LP)
Wednesday 15/11-2023: Lecture: Matching Methods (VR)
Room: KL2.53
Friday 17/11-2023: Workshop - Matching (NN)
Wednesday 22/11-2023: Lecture: Instrumental Variables (VR)
Room: KL2.53
Friday 24/11-2023: Workshop - Instrumental Variables (LP)
Wednesday 29/11-2023: Lecture: Panel Data Models (VR)
Room: KL2.53
Friday 01/12-2023: Workshop - Panel Data (LP)
Wednesday 06/12-2023: Lecture: Policy evaluation methods (HCK/VR) Room: KL2.53
Friday 08/12-2023: Workshop – Policy evaluation (LP)
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.
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.
Exam
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 econometric methods taught in the course, preferably based on a replication of a published paper within each student’s area of interest. Students will receive guidance in locating papers and associated data for the assignment. A 1-page description specifying key variables for the analysis and research question addressed will be due by November 15. 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 14.
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 econometric methods taught in the course, preferably based on a replication of a published paper within each student’s area of interest. Students will receive guidance in locating papers and associated data for the assignment. A 1-page description specifying key variables for the analysis and research question addressed will be due by November 15. 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 14.
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).
• 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.
Select payment methods:
CBS students: Choose CBS PhD students and the course fee will be deducted from your PhD budget.
Students from other Danish universities: Choose Danish Electronic Invoice (EAN). Fill in your EAN number, attention and possible purchase (project) order number. Do you not pay by EAN number please choose Invoice to pay via electronic bank payment (+71).
Students from foreign universities: Choose Payment Card. Are you not able to pay by credit card please choose Invoice International to pay via bank transfer.
Please note that your registration is binding after the registration deadline.
CBS students: Choose CBS PhD students and the course fee will be deducted from your PhD budget.
Students from other Danish universities: Choose Danish Electronic Invoice (EAN). Fill in your EAN number, attention and possible purchase (project) order number. Do you not pay by EAN number please choose Invoice to pay via electronic bank payment (+71).
Students from foreign universities: Choose Payment Card. Are you not able to pay by credit card please choose Invoice International to pay via bank transfer.
Please note that your registration is binding after the registration deadline.
Event Location
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Organizer Contact Information
CBS PhD School
Nina Iversen
Phone: +45 3815 2475
ni.research@cbs.dk
Registration
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Organizer Contact Information
CBS PhD School
Nina Iversen
Phone: +45 3815 2475
ni.research@cbs.dk