Test Bank for Introductory Econometrics A Modern Approach, 6th Edition

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Product details:

• ISBN-10 ‏ : ‎ 130527010X
• ISBN-13 ‏ : ‎ 978-1305270107
• Author: Jeffrey M. Wooldridge

Discover how empirical researchers today actually consider and apply econometric methods with the practical approach in Wooldridge’s INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 6E. Unlike traditional texts, this book uniquely demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions in business, policy evaluation, and forecasting. INTRODUCTORY ECONOMETRICS is organized around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 intriguing data sets, available in six formats. Updates introduce the latest emerging developments in the field. Gain a full understanding of the impact of econometrics in practice today with the insights and applications found only in INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 6E.

Table Of Contents:

1. Ch 1: The Nature of Econometrics and Economic Data
2. 1-1 What is Econometrics?
3. 1-2 Steps in Empirical Economic Analysis
4. 1-3 The Structure of Economic Data
5. 1-4 Causality and the Notion of Ceteris Paribus in Econometric Analysis
6. Summary
7. Key Terms
8. Problems
9. Computer Exercises
10. Part 1: Regression Analysis with Cross-Sectional Data
11. Ch 2: The Simple Regression Model
12. 2-1 Definition of the Simple Regression Model
13. 2-2 Deriving the Ordinary Least Squares Estimates
14. 2-3 Properties of OLS on Any Sample of Data
15. 2-4 Units of Measurement and Functional Form
16. 2-5 Expected Values and Variances of the OLS Estimators
17. 2-6 Regression through the Origin and Regression on a Constant
18. Summary
19. Key Terms
20. Problems
21. Computer Exercises
22. Appendix 2A
23. Ch 3: Multiple Regression Analysis: Estimation
24. 3-1 Motivation for Multiple Regression
25. 3-2 Mechanics and Interpretation of Ordinary Least Squares
26. 3-3 The Expected Value of the OLS Estimators
27. 3-4 The Variance of the OLS Estimators
28. 3-5 Efficiency of OLS: The Gauss-Markov Theorem
29. 3-6 Some Comments on the Language of Multiple Regression Analysis
30. Summary
31. Key Terms
32. Problems
33. Computer Exercises
34. Appendix 3A
35. Ch 4: Multiple Regression Analysis: Inference
36. 4-1 Sampling Distributions of the OLS Estimators
37. 4-2 Testing Hypotheses about a Single Population Parameter: The t Test
38. 4-3 Confidence Intervals
39. 4-4 Testing Hypotheses about a Single Linear Combination of the Parameters
40. 4-5 Testing Multiple Linear Restrictions: The F Test
41. 4-6 Reporting Regression Results
42. Summary
43. Key Terms
44. Problems
45. Computer Exercises
46. Ch 5: Multiple Regression Analysis: OLS Asymptotics
47. 5-1 Consistency
48. 5-2 Asymptotic Normality and Large Sample Inference
49. 5-3 Asymptotic Efficiency of OLS
50. Summary
51. Key Terms
52. Problems
53. Computer Exercises
54. Appendix 5A
55. Ch 6: Multiple Regression Analysis: Further Issues
56. 6-1 Effects of Data Scaling on OLS Statistics
57. 6-2 More on Functional Form
58. 6-3 More on Goodness-of-Fit and Selection of Regressors
59. 6-4 Prediction and Residual Analysis
60. Summary
61. Key Terms
62. Problems
63. Computer Exercises
64. Appendix 6A
65. Ch 7: Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables
66. 7-1 Describing Qualitative Information
67. 7-2 A Single Dummy Independent Variable
68. 7-3 Using Dummy Variables for Multiple Categories
69. 7-4 Interactions Involving Dummy Variables
70. 7-5 A Binary Dependent Variable: The Linear Probability Model
71. 7-6 More on Policy Analysis and Program Evaluation
72. 7-7 Interpreting Regression Results with Discrete Dependent Variables
73. Summary
74. Key Terms
75. Problems
76. Computer Exercises
77. Ch 8: Heteroskedasticity
78. 8-1 Consequences of Heteroskedasticity for OLS
79. 8-2 Heteroskedasticity-Robust Inference after OLS Estimation
80. 8-3 Testing for Heteroskedasticity
81. 8-4 Weighted Least Squares Estimation
82. 8-5 The Linear Probability Model Revisited
83. Summary
84. Key Terms
85. Problems
86. Computer Exercises

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