889953

Course

Applied Econometrics for Researchers - Fall 2017

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

Adrian L. Mérida Gutierrez, Teaching assistant, Department of Innovation and Organizational Economics, Copenhagen Business School, E-mail almg.ino@cbs.dk

Course Coordinator

H. C. Kongsted

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.

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.

Course content

Teaching style

Lectures, workshops, home exercises, student presentations of home exercises.

Lecture plan

Morning sessions 9.00-12.00
Afternoon sessions 13.00-16.00

(dates are subject to change)

28/09-2017 Morning: Afternoon	Introduction to econometrics and Stata essentials (HCK), room: K3.41 Ordinary Least Squares (HCK), room: K3.41
5/10-2017 Morning: Afternoon:	Dummy Variables and Moderation Effects (HCK), room: K3.41 Workshop – Application and interpretation of OLS (AM), room: SP 107
12/10-2017 Morning: Afternoon:	Logit and Probit Models (HCK), room: K3.41 Workshop -Interactions and Non-Linear Models (AM), room: SP 107
26/10-2017 Morning: Afternoon:	Ordered, Multinomial and Count Data Models (VR), room K3.41 Workshop - Logit, Probit, Count Data (AM), room: SP 107
2/11-2017 Morning: Afternoon:	Identification, Sampling and Method Biases (HCK), room: K 3.41 No session
9/11-2017 Morning: Afternoon:	Matching Mehods (VR), room K3.41 Workshop - Matching (AM), room SP 107
16/11-2017 Morning: Afternoon:	Attrition and Selection Models (VR), room: K3.41 Workshop - Selection and Attrition (AM), room: SP 107
23/11-2017 Morning: 24/11-2017 Morning:	Instrumental Variables (VR) room 3.41 Workshop - Instrumental Variables (AM), room: SP 107
30/11-2017 Morning: Afternoon:	Panel Data Models (VR), room: K3.41 Workshop - Panel Data (AM), room: SP 107
7/12-2017 Morning: Afternoon:	Duration models (VR), room: K3.41 Workshop – Exam preparations (AM/HCK), room: SP 107
14/12-2017	Exam, room: SP 107

Learning objectives

Exam

Students are expected to participate in all lectures. There is a 4 hour written exam in week 50

Other

Start date

28/09/2017

End date

07/12/2017

Level

PhD

ECTS

7.5

Language

English

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

Fee

9,750

Minimum number of participants

Maximum number of participants

Location

Please see the lecture plan

Contact information

Bente S. Ramovic
bsr.research@cbs.dk
Tel +45 3815 3138

Registration deadline

15/08/2017

Please notice that registration is binding after the registration deadline

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