Instant download Test Bank for Introductory Econometrics A Modern Approach, 6th Edition pdf docx epub after payment.
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:
- Ch 1: The Nature of Econometrics and Economic Data
- 1-1 What is Econometrics?
- 1-2 Steps in Empirical Economic Analysis
- 1-3 The Structure of Economic Data
- 1-4 Causality and the Notion of Ceteris Paribus in Econometric Analysis
- Summary
- Key Terms
- Problems
- Computer Exercises
- Part 1: Regression Analysis with Cross-Sectional Data
- Ch 2: The Simple Regression Model
- 2-1 Definition of the Simple Regression Model
- 2-2 Deriving the Ordinary Least Squares Estimates
- 2-3 Properties of OLS on Any Sample of Data
- 2-4 Units of Measurement and Functional Form
- 2-5 Expected Values and Variances of the OLS Estimators
- 2-6 Regression through the Origin and Regression on a Constant
- Summary
- Key Terms
- Problems
- Computer Exercises
- Appendix 2A
- Ch 3: Multiple Regression Analysis: Estimation
- 3-1 Motivation for Multiple Regression
- 3-2 Mechanics and Interpretation of Ordinary Least Squares
- 3-3 The Expected Value of the OLS Estimators
- 3-4 The Variance of the OLS Estimators
- 3-5 Efficiency of OLS: The Gauss-Markov Theorem
- 3-6 Some Comments on the Language of Multiple Regression Analysis
- Summary
- Key Terms
- Problems
- Computer Exercises
- Appendix 3A
- Ch 4: Multiple Regression Analysis: Inference
- 4-1 Sampling Distributions of the OLS Estimators
- 4-2 Testing Hypotheses about a Single Population Parameter: The t Test
- 4-3 Confidence Intervals
- 4-4 Testing Hypotheses about a Single Linear Combination of the Parameters
- 4-5 Testing Multiple Linear Restrictions: The F Test
- 4-6 Reporting Regression Results
- Summary
- Key Terms
- Problems
- Computer Exercises
- Ch 5: Multiple Regression Analysis: OLS Asymptotics
- 5-1 Consistency
- 5-2 Asymptotic Normality and Large Sample Inference
- 5-3 Asymptotic Efficiency of OLS
- Summary
- Key Terms
- Problems
- Computer Exercises
- Appendix 5A
- Ch 6: Multiple Regression Analysis: Further Issues
- 6-1 Effects of Data Scaling on OLS Statistics
- 6-2 More on Functional Form
- 6-3 More on Goodness-of-Fit and Selection of Regressors
- 6-4 Prediction and Residual Analysis
- Summary
- Key Terms
- Problems
- Computer Exercises
- Appendix 6A
- Ch 7: Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables
- 7-1 Describing Qualitative Information
- 7-2 A Single Dummy Independent Variable
- 7-3 Using Dummy Variables for Multiple Categories
- 7-4 Interactions Involving Dummy Variables
- 7-5 A Binary Dependent Variable: The Linear Probability Model
- 7-6 More on Policy Analysis and Program Evaluation
- 7-7 Interpreting Regression Results with Discrete Dependent Variables
- Summary
- Key Terms
- Problems
- Computer Exercises
- Ch 8: Heteroskedasticity
- 8-1 Consequences of Heteroskedasticity for OLS
- 8-2 Heteroskedasticity-Robust Inference after OLS Estimation
- 8-3 Testing for Heteroskedasticity
- 8-4 Weighted Least Squares Estimation
- 8-5 The Linear Probability Model Revisited
- Summary
- Key Terms
- Problems
- Computer Exercises
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