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Textbook:
Empirical Research in Accounting: Tools and Methods
Author(s): Ian D. Gow, Tongqing Ding
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.
Contents
I. Foundations
1. Introduction
1.1. Structure of the book
1.2. Setting up your computer
2. Describing data
2.1. Introduction to R
2.2. Exploring data
2.3. Basic data analysis and statistics
2.4. Reproducible research
2.5. Further reading
2.6. Exercises
3. Regression fundamentals
3.1. Introduction
3.2. Running regressions in R
3.3. Frisch-Waugh-Lovell theorem
3.4. Further reading
4. Causal inference
4.1. Econometrics
4.2. Basic causal relations
4.3. Causal diagrams: Formalities
4.4. Discrimination and bias
4.5. Causal diagrams: Application in accounting
4.6. Further reading
5. Statistical inference
5.1. Some observations
5.2. Data-generating processes
5.3. Hypothesis testing
5.4. Differences in means
5.5. Inference with regression
5.6. Dependence
5.7. Further reading
6. Financial statements: A first look
6.1. Setting up WRDS
6.2. Financial statement data
6.3. Exercises
7. Linking databases
7.1. Firm identifiers
7.2. The CRSP database
7.3. Linking CRSP and Compustat
7.4. All about CUSIPs
8. Financial statements: A second look
8.1. Core attributes of financial statements
8.2. Balance sheets
8.3. Within-statement articulation
8.4. Across-statement articulation
8.5. Missing R&D
9. Importing data
9.1. Reading (seemingly) non-tabular data
9.2. Extracting data from messy formats
9.3. Further reading
II. Capital Markets Research
10. FFJR
10.1. Efficient capital markets
10.2. Stock splits
10.3. Dividend policy
10.4. Replication of FFJR
10.5. Capital markets research in accounting
11. Ball and Brown (1968)
11.1. Principal results of Ball and Brown (1968)
11.2. Replicating Ball and Brown (1968)
12. Beaver (1968)
12.1. Market reactions to earnings announcements
12.2. A re-evaluation of Beaver (1968)
12.3. Discussion questions
13. Event studies
13.1. Overview
13.2. The modern event study
13.3. Event studies and regulation
14. Post-earnings announcement drift
14.1. Fiscal years
14.2. Quarterly data
14.3. Time-series properties of earnings
14.4. Post-earnings announcement drift
15. Accruals
15.1. Sloan (1996)
15.2. Measuring accruals
15.3. Simulation analysis
15.4. Replicating Sloan (1996)
15.5. Accrual anomaly
16. Earnings management
16.1. Measuring earnings management
16.2. Evaluating measures of earnings management
16.3. Power of tests of earnings management
III. Causal Inference
17. Natural experiments
17.1. Randomized experiments
17.2. Natural experiments
17.3. Recognition versus disclosure
17.4. Michels (2017)
17.5. Discussion questions
18. Causal mechanisms
18.1. John Snow and cholera
18.2. Smoking and heart disease
18.3. Causal mechanisms in accounting research
18.4. Discussion questions
19. Natural experiments revisited
19.1. A replication crisis?
19.2. The Reg SHO experiment
19.3. Analysing natural experiments
19.4. Evaluating natural experiments
19.5. The parallel trends assumption
19.6. Indirect effects of Reg SHO
19.7. Statistical inference
19.8. Causal diagrams
19.9. Causal mechanisms
19.10. Two-step regressions
20. Instrumental variables
20.1. The canonical causal diagram
20.2. Estimation
20.3. Reasoning about instruments
20.4 “Bullet-proof” instruments
20.5. Causal diagrams: An application
20.6. Further reading
20.7. Discussion questions and exercises
21. Panel data
21.1. Analysis of simulated data
21.2. Voluntary disclosure
21.3. Further reading
22. Regression discontinuity designs
22.1. Sharp RDD
22.2. Fuzzy RDD
22.3. Other issues
22.4. Sarbanes-Oxley Act
22.5. RDD in accounting research
22.6. Further reading
22.7. Discussion questions
IV. Additional Topics
23. Beyond OLS
23.1. Complexity and voluntary disclosure
23.2. Generalized linear models
23.3. Application: Complexity and voluntary disclosure
23.4. Further reading
23.5. Appendix: Maintaining a repository of SEC index files
24. Extreme values and sensitivity analysis
24.1. Leone et al. (2019)
24.2. Call et al. (2018)
24.3. Sensitivity analysis
24.4. Appendix: Simulation study from Leone et al. (2019)
25. Matching
25.1. Background on auditor choice
25.2. Simulation analysis
25.3. Replication of Lawrence et al. (2011)
25.4. DeFond et al. (2017)
25.5. Further reading
25.6. Discussion questions
26. Prediction
26.1. Prediction in research
26.2. Predicting accounting fraud
26.3. Model foundations
26.4. Performance metrics
26.5. Penalized models
26.6. Ensemble methods
26.7. Testing models
26.8. Further reading
26.9. Discussion questions and exercises
V. Appendices
A. Linear algebra
A.1. Vectors
A.2. Matrices
A.3. The OLS estimator
A.4. Further reading
B. SQL primer
B.1. What are SQL and dplyr?
B.2. SQL terms SELECT and FROM
B.3. SQL WHERE
B.4. SQL ORDER BY
B.5. SQL approach to mutate()
B.6. SQL GROUP BY and aggregates
C. Research computing overview
C.1. Languages
C.2. Data management
D. Running PostgreSQL
D.1. Setting up a personal server
D.2. Setting up a shared server
E. Making a parquet repository
E.1. Data management approaches
E.2. Organizing data
E.3. Canonical WRDS data
E.4. Converting WRDS data to parquet
E.5. Working with parquet files
E.6. Creating a parquet library
References
Index
Ian D. Gow, Tongqing Ding - Empirical Research in Accounting_ Tools and Methods.part1.rar
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Ian D. Gow, Tongqing Ding - Empirical Research in Accounting_ Tools and Methods.part2.rar
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