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Applied Econometrics (from week 42)

Faculty
Keld Laursen, Professor, Department of Innovation and Organizational Economics, CBS (kl.ino@cbs.dk), Toke Reichstein, Associate Professor, Department of Innovation and Organizational Economics, CBS (tr.ino@cbs.dk), Larissa Rabbiosi, Assistant Professor, Department of Strategic Management and Globalization, CBS (lr.smg@cbs.dk), and Jing Chen, Post Doc, Department of Innovation and Organizational Economics, CBS (jc.ino@cbs.dk)
Course Coordinator
Associate Professor, Toke Reichstein (tr.ino@cbs.dk)
Prerequisite
The course progresses at a very rapid pace. Students are consequently required to have knowledge on descriptive statistics, probability, and probability distributions prior to attending. Knowledge on means, medians, modes, standard deviations, variances, skewness, kurtosis, and the interpretation of these is a prerequisite. The course will utilize STATA in illustrating examples and in the exercise and workshop sessions. It is, however, not assumed that the students have prior experience with STATA.
Aim of the course
The overall aim of the course is to provide econometric analytical tools to PhD students enabling them to conduct rigorous and analytically correct statistical investigations and research. 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, students will be equipped to tackle econometric challenges, conduct rigorous econometric research and to discuss and comment on econometric research 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 attrition (selection bias) and endogeneity problems. Furthermore, the student will be exposed to panel data estimation in the form of fixed effects, random effects, 1st differencing, LSDV models and duration models. 
Course content, structure and teaching
  • Econometrics, Economic Data, Descriptive Statistics, Stata and simple operations
  • Sampling, Data Representativeness and common method bias
  • Ordinary Least Square & Specification
  • Non-Linear Regressions and Tobit Corner Solutions
  • Factor Analysis
  • Moderation Effects, Prediction and Size Effects
  • Logit and Probit Models
  • Interactions in Logits and Probit Models
  • Ordered Multinomial and Multinomial Logit Models
  • Count Variable Models
  • Attrition and Selection Correction
  • Endogeneity and Instrumental variables Regression
  • Panel Data Estimations
  • Duration Models
Course literature
  • Ai C., & Norton E.C., (2003). Interaction terms in logit and probit models. Economics Letters, 80 123-129.
  • Anthony, D.  (2005), Cooperation in Microcredit Borrowing Groups: Identity, Sanctions, and Reciprocity in the Production of Collective Goods, American Sociological Review, 70(3), 496-515
  • Baron, R. M., & Kenny, D. A. (1986). The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
  • Finney J. W., Mitchell R. E., Cronkite R. C., & Moos R. H. (1984). Methodological issues in estimating main and interactive effects: Examples from coping/social support and stress field. Journal of Health and Social Behavior, 25(1), 85-98.
  • Forza C. (2002). Survey research in operations management: a process-based perspective. International Journal of Operations & Production Management, 22(2), 152-194.
  • Guilhem, B. (2008), Controlling for Endogeneity with Instrumental Variables in Strategic Management Research, Strategic Organization, 6(3), pp. 285-327
  • Hamilton, B. H. & Nickerson, J. A. (2003), Correcting for Endogeneity in Strategic Management Research, Strategic Organization, 1(1), pp. 51-78
  • Hoetker G., (2007), The use of logit and probit models in strategic management research: critical issues. Strategic Management Journal, (28) pp. 331-343.
  • Kiefer N.M. (1988), Economic duration data and hazard functions, Journal of Economic Literature, Vol. 26, pp. 646-679
  • 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
  • Khoshgoftaar T.M., Gao K., Szabo R.M. (2005), Comparing software fault predictions of pure and zero-inflated Poisson regression models, International Journal of Systems Science, 36(11), 705-715
  • McDowell, A. (2003), Form the Help Desk: Hurdle Models, The Stata Journal, Vol 3(2), p. 178-184
  • Mitchell M.N., & Chen X., (2005). Visualizing main effects and interactions for binary logit models. The Stata Journal, 5(1), 64-82.
  • Murray, M. P. (2006), Avoiding Invalid Instruments and Coping with Weak Instruments, Journal of Econmic Perspectives, 20(4), pp. 111-132
  • Norton E.C., H. Wang, & Ai C., (2004). Computing interaction effects and standard errors in logit and probit models. The Stata Journal, 4(2) pp. 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
Enrolment
Applications should be sent as e-mail to:
Katja Høeg Tingleff (e-mail: kht.research@cbs.dk)
No later than October 03, 2011.
Please remember to state your name, email, Department and University.

Sidst opdateret af Sarah Biel 07.10.2011