Real Econometrics 2nd Edition By Michael Bailey – Ebook PDF Instant Download/Delivery: 0190857463 , 978-0190857462
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ISBN 10: 0190857463
ISBN 13: 978-0190857462
Author: Michael Bailey
An engaging and practical introduction to econometrics, Real Econometrics: The Right Tools to Answer Important Questions, offers thorough coverage of the most frequently used methods of analysis. Grounded in contemporary understandings of causal inference, the text invites students to extract meaningful information about important economic policy issues from available data. Bailey’s emphasis on practical applications, combined with a lively and conversational narrative and a diverse array of examples and case studies, provides students with a solid foundation in the analytical tools they will use throughout their academic and professional careers. The second edition includes new conceptual exercises, revised appendices, and additional code and guidance for R software.
Real Econometrics 2nd Table of contents:
ACKNOWLEDGMENTS
1 The Quest for Causality
1.1 The Core Model
1.2 Two Major Challenges: Randomness and Endogeneity
1.3 Randomized Experiments as the Gold Standard
Conclusion
Key Terms
2 Stats in the Wild: Good Data Practices
2.1 Know Our Data
2.2 Replication
2.3 Statistical Software
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
I The Ols Framework
3 Bivariate OLS: The Foundation of Econometric Analysis
3.1 Bivariate Regression Model
3.2 Random Variation in Coefficient Estimates
3.3 Endogeneity and Bias
3.4 Precision of Estimates
3.5 Probability Limits and Consistency
3.6 Solvable Problems: Heteroscedasticity and Correlated Errors
3.7 Goodness of Fit
3.8 Outliers
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
4 Hypothesis Testing and Interval Estimation: Answering Research Questions
4.1 Hypothesis Testing
4.2 t Tests
4.3 p Values
4.4 Power
4.5 Straight Talk about Hypothesis Testing
4.6 Confidence Intervals
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
5 Multivariate OLS: Where the Action Is
5.1 Using Multivariate OLS to Fight Endogeneity
5.2 Omitted Variable Bias
5.3 Measurement Error
5.4 Precision and Goodness of Fit
5.5 Standardized Coefficients
5.6 Hypothesis Testing about Multiple Coefficients
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
6 Dummy Variables: Smarter than You Think
6.1 Using Bivariate OLS to Assess Difference of Means
6.2 Dummy Independent Variables in Multivariate OLS
6.3 Transforming Categorical Variables to Multiple Dummy Variables
6.4 Interaction Variables
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
7 Specifying Models
7.1 Quadratic and Polynomial Models
7.2 Logged Variables
7.3 Post-Treatment Variables
7.4 Model Specification
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
II The Contemporary Econometric Toolkit
8 Using Fixed Effects Models to Fight Endogeneity in Panel Data and Difference-in-Difference Models
8.1 The Problem with Pooling
8.2 Fixed Effects Models
8.3 Working with Fixed Effects Models
8.4 Two-Way Fixed Effects Model
8.5 Difference-in-Difference
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
9 Instrumental Variables: Using Exogenous Variation to Fight Endogeneity
9.1 2SLS Example
9.2 Two-Stage Least Squares (2SLS)
9.3 Multiple Instruments
9.4 Quasi and Weak Instruments
9.5 Precision of 2SLS
9.6 Simultaneous Equation Models
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
10 Experiments: Dealing with Real-World Challenges
10.1 Randomization and Balance
10.2 Compliance and Intention-to-Treat Models
10.3 Using 2SLS to Deal with Non-compliance
10.4 Attrition
10.5 Natural Experiments
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
11 Regression Discontinuity: Looking for Jumps in Data
11.1 Basic RD Model
11.2 More Flexible RD Models
11.3 Windows and Bins
11.4 Limitations and Diagnostics
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
III Limited Dependent Variables
12 Dummy Dependent Variables
12.1 Linear Probability Model
12.2 Using Latent Variables to Explain Observed Variables
12.3 Probit and Logit Models
12.4 Estimation
12.5 Interpreting Probit and Logit Coefficients
12.6 Hypothesis Testing about Multiple Coefficients
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
IV Advanced Material
13 Time Series: Dealing with Stickiness over Time
13.1 Modeling Autocorrelation
13.2 Detecting Autocorrelation
13.3 Fixing Autocorrelation
13.4 Dynamic Models
13.5 Stationarity
Conclusion
Further Reading
Key Terms
Computing Corner
Exercises
14 Advanced OLS
14.1 How to Derive the OLS Estimator and Prove Unbiasedness
14.2 How to Derive the Equation for the Variance of 1
14.3 Calculating Power
14.4 How to Derive the Omitted Variable Bias Conditions
14.5 Anticipating the Sign of Omitted Variable Bias
14.6 Omitted Variable Bias with Multiple Variables
14.7 Omitted Variable Bias due to Measurement Error
14.8 Collider Bias with Post-Treatment Variables
Conclusion
Further Reading
Key Term
Computing Corner
Exercises
15 Advanced Panel Data
15.1 Panel Data Models with Serially Correlated Errors
15.2 Temporal Dependence with a Lagged Dependent Variable
15.3 Random Effects Models
Conclusion
Further Reading
Key Term
Computing Corner
Exercises
16 Conclusion: How to Be an Econometric Realist
Further Reading
APPENDICES: MATH AND PROBABILITY BACKGROUND
A Summation
B Expectation
C Variance
D Covariance
E Correlation
F Probability Density Functions
G Normal Distributions
H Other Useful Distributions
I Sampling
Further Reading
Key Terms
Computing Corner
CITATIONS AND ADDITIONAL NOTES
GUIDE TO REVIEW QUESTIONS
BIBLIOGRAPHY
PHOTO CREDITS
GLOSSARY
INDEX
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