Empirical Research in Accounting: Tools and Methods(Chapman and Hall/CRC Series on Statistics in Business and Economics)

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. Starting with foundations in statistics, econometrics, causal inference, and institutional knowledge of accounting and finance, the book moves on to an in-depth coverage of the core papers in capital market research. The latter half of the book examines contemporary approaches to research design and empirical analysis, including natural experiments, instrumental variables, fixed effects, difference-in-differences, regression discontinuity design, propensity-score matching, and machine learning. Readers of the book will develop deep data analysis skills using modern tools. Extensive replication and simulation analysis is included throughout. Key Features: Extensive coverage of empirical accounting research over more than 50 years. Integrated coverage of statistics and econometrics, institutional knowledge, and research design. Numerous replications and a dozen simulation analyses to immerse readers in papers and empirical analysis. All tables and figures in the book can be reproduced by readers using included code. Easy-to-use templates facilitate hands-on exercises and introduce reproduceable research concepts. (Solutions available to instructors.)

Table of Contents:
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

About the Author :
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.