This textbook provides the foundation for a course that takes PhD students in empirical accounting research from the very basics of statistics, data analysis, and causal inference up to the point at which they conduct their own research.
This textbook provides the foundation for a course that takes PhD students in empirical accounting research from the very basics of statistics, data analysis, and causal inference up to the point at which they conduct their own research.
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.
Inhaltsangabe
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
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
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