Applied Econometrics (from week 40)

Faculty
Keld Laursen, Professor, Department of Innovation and Organizational Economics, Copenhagen Business School, Denmark, Email: kl.ino@cbs.dk, Larissa Rabbiosi, Assistant Professor, Centre for Strategic Management and Globalization, Copenhagen Business School. Email: lr.smg@cbs.dk and Toke Reichstein, Associate Professor, Department of Innovation and Organizational Economics, Copenhagen Business School, Email: tr.ino@cbs.dk

Course Coordinator
Toke Reichstein

The course requires that the students have very basic qualifications in statistics. We only assume the 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.

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 attrition (selection bias) and endogeneity problems. Furthermore, the student will be exposed to panel data estimation in the form of fixed effects, random effects and duration models.

[K]: Keld Laursen, [L]: Larissa Rabbiosi, [T]: Toke Reichstein

Econometrics 1: Week 40, Friday 8th of October [T]
Econometrics, Economic Data, Universal Statistics, Stata and Simple Operations
• Wooldridge (2009): Chapter 1
• Wooldridge (2009): App. B1-B5 (except 732-733), C6 (Except 780-781)

Econometrics 2: Week 41, Friday 15th of October [L]
Sampling, Data Representativeness and Common Method Bias
• Forza C. (2002)
• Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2003)

Econometrics 3: Week 42, Friday 22nd of October [T]
Ordinary Least Square & Specification
• Wooldridge (2009): Chapter 2
• Wooldridge (2009): Sections 3.1-3.4, 4.1-4.3, 4.5, 4.6, 5.1, 7.4, 8.1-8.3

Econometrics 4: Week 43, Friday 29th of October [L]
Moderation Effects, Prediction and Size Effects
• Finney J. W., Mitchell R. E., Cronkite R. C., & Moos R. H. (1984)
• Jaccard J., Wan C. K., & Turrisi R. (1990)

Econometrics 5: Week 44, Friday 5th of November [T]
Non-Linear Regressions and Tobit Corner Solutions
• Wooldridge (2009): Sections 6.2, 6.4, 17.2 and App. A4

Econometrics 6: Week 46, Friday 19th of November [L]
Factor Analysis

Econometrics 6.1: Week 46, Friday 19th of November [L&T]
Going through home exercises from sessions 1, 2, 3, 4, and 5

Econometrics 7: Week 47, Friday 26th of November [K]
Logit and Probit Models
• Wooldridge(2009): Section 17.1
• Wooldridge(2002): Sections 15.1-15.3, 15.5.2, 15.6
• Hoetker G., (2007)

Econometrics 8: Week 48, Friday 3 rd of December [L]
Interactions in Logits and Probit Models
• Ai C., & Norton E.C. (2003)
• Hoetker G. (2007)
• Norton E.C., H. Wang, & Ai C. (2004)

Econometrics 9: Week 49, Friday 10th of December [K]
Ordered Multinomial and Multinomial Logit Models
• Laursen, K. and Salter, A. (2004)
• Reichstein, T. & Salter, A. (2006)

Econometrics 9.1: Week 49, Friday 10th of December [K&L]
Going through home exercises from sessions 6, 7, 8

Econometrics 10: Week 2, Friday 14th of January [T]
Count Variable Models
• Wooldridge(2002): Section 19.1, 19.2, 19.3
• Khoshgoftaar T.M., Gao K., Szabo R.M. (2005)
• McDowell, A. (2003)
• Denise Anthony (2005)

Econometrics 11: Week 4, Friday 28th of January [T]
Attrition and Selection Correction
• Wooldridge (2009): pp. 606-613
• Wooldridge (2002): section 17.1-17.2

Econometrics 12: Week 5, Friday 4th of February [K]
Endogeneity and Instrumental Variables Regression
• Wooldridge (2002): Chapter 5
• Wooldridge (2009): pp. 506-520 & 527-531
• Guilhem (2006)
• Hamilton & Nickerson (2003)
• Murray (2006)

Econometrics 13: Week 6, Friday 11th of February [L&T]
Panel Data Estimations
• Wooldridge (2002) Chapter 1 & 10
• Wooldridge (2009) pp. 5-12 & Chapter 14

Econometrics 13.1: Week 6, Friday 11th of February [K&T]
Going through home exercises from sessions 9, 10, 11, and 12

Econometrics 14: Week 7, Friday 18th of February [L&T]
Panel Data Estimations
• Wooldridge (2002) Chapter 1 & 10
• Wooldridge (2009) pp. 5-12 & Chapter 14

Econometrics 15: Week 8, Friday 25th of February [L&T]
Duration Models
• Wooldridge 2002, Chapter 20
• Kiefer N.M. (1988)

Econometrics 15.1: Week 8, Friday 25th of February [L&T]
Going through home exercises from sessions 13 and 14

There is a 4 hour exam in week 9, 2011 resembling the course exercises.

Lectures, exercises, student presentations.

• 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, 28 331-343.

• Jaccard J., Wan C. K., & Turrisi R. (1990). The detection and interpretation of interaction effects between continuous variables in multiple regression. Multivariate Behavioral Research, 25(4), 467-478.

• 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) 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

Deadline: <15.09.2010>
Applications should be sent as e-mail to: Shi Hua Chen Kold
Department of Innovation and Organizational Economics, E-mail: shc.ino@cbs.dk

Please remember to state your name, email, Department and University

Duration:

Fridays 8:00-10:45 on week 40-44, 46-49, 2, 4-8, 2010 & 2011 (total of 15 lectures)
Fridays 13:30-15:20 on week 46, 49, 6 and 8 2010 &2011 (total of 4 exercises)