1st Edition
Empirical Research in Accounting Tools and Methods
Preface
Part 1: Foundations
1. Introduction
2. Describing data
3. Regression fundamentals
4. Causal inference
5. Statistical inference
6. Financial statements: A first look
7. Linking databases
8. Financial statements: A second look
9. Importing data
Part 2: Capital Markets Research
10. FFJR
11. Ball and Brown (1968)
12. Beaver (1968)
13. Event studies
14. Post-earnings announcement drift
15. Accruals
16. Earnings management
Part 3: Causal Inference
17. Natural experiments
18. Causal mechanisms
19. Natural experiments revisited
20. Instrumental variables
21. Panel data
22. Regression discontinuity designs
Part 4: Additional Topics
23. Beyond OLS
24. Extreme values and sensitivity analysis
25. Matching
26. Prediction
Appendices
A. Linear algebra
B. SQL primer
C. Research computing overview
D. Running PostgreSQL
E. Making a parquet repository
References
Index
Biography
Ian D. Gow is a professor at the University of Melbourne, where he teaches several courses, including courses based on this book . Ian previously served on the faculties of Harvard Business School, Northwestern University, and Yale. Ian’s recent research focuses on causal inference and empirical methods. Ian has a PhD from Stanford, an MBA from Harvard and BCom and LLB degrees from the University of New South Wales.
Tongqing (Tony) Ding is a senior lecturer at the University of Melbourne, where he teaches courses on data analytics, financial statement analysis, and corporate reporting. Tony’s research focuses on corporate governance, financial reporting and disclosure, ESG, and data analytics. Tony has PhD and MS degrees from the University of Colorado and degrees from Shanghai Jiao Tong University.
