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964106
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Course |
Applied Econometrics for Researchers - Fall 2018
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Faculty |
Hans Christian Kongsted, Professor, Department of Innovation and Organizational Economics, Copenhagen Business School, Email: hck.ino@cbs.dk
Vera Rocha, Tenure-track Assistant Professor, Department of Innovation and Organizational Economics, Copenhagen Business School, Email: vr.ino@cbs.dk
Hadar Gafni, Teaching assistant, Department of Innovation and Organizational Economics, Copenhagen Business School, E-mail hg.ino@cbs.dk
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Course Coordinator |
H. C. Kongsted
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Prerequisites |
The course requires that students have basic qualifications in statistics. It is assumed that students know how to calculate mean values and standard deviations and how to interpret these. Knowledge of any particular programming language or estimation procedures is not required even if Stata is used in the course. Some programming experience is, however, an advantage. Students are expected to follow the entire program and to attend the final written exam. Students attending the course will need access to Stata. STATA is available for usage at the CBS computer room. Note that CBS does not offer licensed Stata to students.
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Aim |
The overall aim of the course is to provide econometric analytical tools 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 taught tool. 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. The student will be equipped with tools ranging from Ordinary Least Square to Limited Dependent Variables Models and Count Models useful for cross section settings. In this context, students will learn how to handle selection bias and endogeneity problems. Furthermore, the student will be exposed to panel data estimation and duration models.
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Course content |
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Teaching style |
Lectures, workshops, home exercises, student presentations of home exercises.
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Lecture plan |
Morning sessions 9.00-12.00 Afternoon sessions 13.00-16.00
(dates are subject to change)
27/09-2018 Morning: Afternoon: |
Introduction to econometrics and Stata essentials (HCK), room: K3.41 Ordinary Least Squares (HCK), room: K3.41 |
4/10-2018 Morning: Afternoon: |
Dummy Variables and Moderation Effects (HCK), room: SPD.4 Augustinusfonden Workshop – Application and interpretation of OLS (HG), room: SP 107 |
11/10-2018 Morning: Afternoon:
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Logit and Probit Models (HCK), room: K1.43 Workshop -Interactions and Non-Linear Models (HG), room: SP 107
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25/10-2018 Morning: Afternoon: |
Ordered, Multinomial and Count Data Models (VR), room K1.43 Workshop - Logit, Probit, Count Data (HG), room: SP 107 |
1/11-2018 Morning: Afternoon: |
Identification, Sampling and Method Biases (HCK), room: K2.53 Workshop - TBA, room SP107 |
8/11-2018 Morning: Afternoon: |
Matching Mehods (VR), room K2.53 Workshop - Matching (HG), room SP 107 |
15/11-2018 Morning: Afternoon: |
Attrition and Selection Models (VR), room: K2.53 Workshop - Selection and Attrition (HG), room: SP 107 |
22/11-2018 Morning: Afternoon: |
Instrumental Variables (VR) room K2.53 Workshop - Instrumental Variables (HG), room: SP 107 |
29/11-2018 Morning: Afternoon: |
Panel Data Models (VR), room: K2.53 Workshop - Panel Data (HG), room: SP 107 |
6/12-2018 Morning: Afternoon:
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Duration models (VR), room: K2.53 Workshop – Exam preparations (HG/HCK), room: SP 107 |
13/12-2018 |
Exam, room: SP 107 |
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Learning objectives |
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Exam |
Students are expected to participate in all lectures. There is a 4 hour written exam in week 50
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Other |
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Start date |
27/09/2018
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End date |
13/12/2018
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Level |
PhD
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ECTS |
7.5
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Language |
English
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Course Literature |
(TITLES ARE SUBJECT TO CHANGE)• Ai C., & Norton E.C., (2003). Interaction terms in logit and probit models. Economics Letters, 80 123-129.• Forza C. (2002). Survey research in operations management: a process-based perspective. International Journal of Operations & Production Management, 22(2), 152-194.• Hoetker G., (2007), The use of logit and probit models in strategic management research: critical issues. Strategic Management Journal, 28 331-343.• Laursen, K. and Salter, A. (2004), Searching high and low: what types of firms use universities as a source of innovation?, Research Policy 33, 1201–1215• 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.• Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2003). Common method bias in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879-903.• Reichstein, T. & Salter, A. (2006), Investigating the Sources of Process Innovation among UK Manufacturing Firms, Industrial and Corporate Change, vol. 15(4), p. 653-682• Wooldridge, J. M. (2009), Introductory Econometrics - A Modern Approach, International Student Edition, 4th Edition, South Western• Wooldridge, J. M. (2002), Econometric Analysis of Cross Section and Panel Data, The MIT Press, Cambridge MA
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Fee |
9,750
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Minimum number of participants |
8
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Maximum number of participants |
12
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Location |
Please see the lecture plan
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Contact information |
Bente S. Ramovic bsr.research@cbs.dk Tel +45 3815 3138
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Registration deadline |
06/08/2018
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Please notice that registration is binding after the registration deadline
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