Introductory Econometrics A Modern Approach 6th Edition Wooldridge Test Bank

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

This book is a standalone book and will not include any access codes. 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 Content:

  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
  87. Ch 9: More on Specification and Data Issues
  88. 9-1 Functional Form Misspecification
  89. 9-2 Using Proxy Variables for Unobserved Explanatory Variables
  90. 9-3 Models with Random Slopes
  91. 9-4 Properties of OLS under Measurement Error
  92. 9-5 Missing Data, Nonrandom Samples, and Outlying Observations
  93. 9-6 Least Absolute Deviations Estimation
  94. Summary
  95. Key Terms
  96. Problems
  97. Computer Exercises
  98. Part 2: Regression Analysis with Time Series Data
  99. Ch 10: Basic Regression Analysis with Time Series Data
  100. 10-1 The Nature of Time Series Data
  101. 10-2 Examples of Time Series Regression Models
  102. 10-3 Finite Sample Properties of OLS under Classical Assumptions
  103. 10-4 Functional Form, Dummy Variables, and Index Numbers
  104. 10-5 Trends and Seasonality
  105. Summary
  106. Key Terms
  107. Problems
  108. Computer Exercises
  109. Ch 11: Further Issues in Using OLS with Time Series Data
  110. 11-1 Stationary and Weakly Dependent Time Series
  111. 11-2 Asymptotic Properties of OLS
  112. 11-3 Using Highly Persistent Time Series in Regression Analysis
  113. 11-4 Dynamically Complete Models and the Absence of Serial Correlation
  114. 11-5 The Homoskedasticity Assumption for Time Series Models
  115. Summary
  116. Key Terms
  117. Problems
  118. Computer Exercises
  119. Ch 12: Serial Correlation and Heteroskedasticity in Time Series Regressions
  120. 12-1 Properties of OLS with Serially Correlated Errors
  121. 12-2 Testing for Serial Correlation
  122. 12-3 Correcting for Serial Correlation with Strictly Exogenous Regressors
  123. 12-4 Differencing and Serial Correlation
  124. 12-5 Serial Correlation-Robust Inference after OLS
  125. 12-6 Heteroskedasticity in Time Series Regressions
  126. Summary
  127. Key Terms
  128. Problems
  129. Computer Exercises
  130. Part 3: Advanced Topics
  131. Ch 13: Pooling Cross Sections across Time: Simple Panel Data Methods
  132. 13-1 Pooling Independent Cross Sections across Time
  133. 13-2 Policy Analysis with Pooled Cross Sections
  134. 13-3 Two-Period Panel Data Analysis
  135. 13-4 Policy Analysis with Two-Period Panel Data
  136. 13-5 Differencing with More Than Two Time Periods
  137. Summary
  138. Key Terms
  139. Problems
  140. Computer Exercises
  141. Appendix 13A
  142. Ch 14: Advanced Panel Data Methods
  143. 14-1 Fixed Effects Estimation
  144. 14-2 Random Effects Models
  145. 14-3 The Correlated Random Effects Approach
  146. 14-4 Applying Panel Data Methods to Other Data Structures
  147. Summary
  148. Key Terms
  149. Problems
  150. Computer Exercises
  151. Appendix 14A
  152. Ch 15: Instrumental Variables Estimation and Two Stage Least Squares
  153. 15-1 Motivation: Omitted Variables in a Simple Regression Model
  154. 15-2 IV Estimation of the Multiple Regression Model
  155. 15-3 Two Stage Least Squares
  156. 15-4 IV Solutions to Errors-in-Variables Problems
  157. 15-5 Testing for Endogeneity and Testing Overidentifying Restrictions
  158. 15-6 2SLS with Heteroskedasticity
  159. 15-7 Applying 2SLS to Time Series Equations
  160. 15-8 Applying 2SLS to Pooled Cross Sections and Panel Data
  161. Summary
  162. Key Terms
  163. Problems
  164. Computer Exercises
  165. Appendix 15A
  166. Ch 16: Simultaneous Equations Models
  167. 16-1 The Nature of Simultaneous Equations Models
  168. 16-2 Simultaneity Bias in OLS
  169. 16-3 Identifying and Estimating a Structural Equation
  170. 16-4 Systems with More Than Two Equations
  171. 16-5 Simultaneous Equations Models with Time Series
  172. 16-6 Simultaneous Equations Models with Panel Data
  173. Summary
  174. Key Terms
  175. Problems
  176. Computer Exercises
  177. Ch 17: Limited Dependent Variable Models and Sample Selection Corrections
  178. 17-1 Logit and Probit Models for Binary Response
  179. 17-2 The Tobit Model for Corner Solution Responses
  180. 17-3 The Poisson Regression Model
  181. 17-4 Censored and Truncated Regression Models
  182. 17-5 Sample Selection Corrections
  183. Summary
  184. Key Terms
  185. Problems
  186. Computer Exercises
  187. Appendix 17A
  188. Appendix 17B
  189. Ch 18: Advanced Time Series Topics
  190. 18-1 Infinite Distributed Lag Models
  191. 18-2 Testing for Unit Roots
  192. 18-3 Spurious Regression
  193. 18-4 Cointegration and Error Correction Models
  194. 18-5 Forecasting
  195. Summary
  196. Key Terms
  197. Problems
  198. Computer Exercises
  199. Ch 19: Carrying out an Empirical Project
  200. 19-1 Posing a Question
  201. 19-2 Literature Review
  202. 19-3 Data Collection
  203. 19-4 Econometric Analysis
  204. 19-5 Writing an Empirical Paper
  205. Summary
  206. Key Terms
  207. Sample Empirical Projects
  208. List of Journals
  209. Data Sources
  210. Appendix A: Basic Mathematical Tools
  211. Appendix B: Fundamentals of Probability
  212. Appendix C: Fundamentals of Mathematical Statistics
  213. Appendix D: Summary of Matrix Algebra
  214. Appendix E: The Linear Regression Model in Matrix Form
  215. Appendix F: Answers to Chapter Questions
  216. Appendix G: Statistical Tables
  217. References
  218. Glossary
  219. Index

 

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