Solution manual for Introductory Econometrics A Modern Approach 5th Edition by Jeffrey Wooldridge – Ebook PDF Instant Download/Delivery: 1111531048 ,9781111531041
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ISBN 10: 1111531048
ISBN 13: 9781111531041
Author: Jeffrey Wooldridge
Solution manual for Introductory Econometrics A Modern Approach 5th Edition Table of contents:
Chapter 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
1-3a. Cross-Sectional Data
1-3b. Time Series Data
1-3c. Pooled Cross Sections
1-3d. Panel or Longitudinal Data
1-3e. A Comment on Data Structures
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
Chapter 2. The Simple Regression Model
2-1. Definition of the Simple Regression Model
2-2. Deriving the Ordinary Least Squares Estimates
2-2a. A Note on Terminology
2-3. Properties of OLS on Any Sample of Data
2-3a. Fitted Values and Residuals
2-3b. Algebraic Properties of OLS Statistics
2-3c. Goodness-of-Fit
2-4. Units of Measurement and Functional Form
2-4a. The Effects of Changing Units of Measurement on OLS Statistics
2-4b. Incorporating Nonlinearities in Simple Regression
2-4c. The Meaning of “Linear” Regression
2-5. Expected Values and Variances of the OLS Estimators
2-5a. Unbiasedness of OLS
2-5b. Variances of the OLS Estimators
2-5c. Estimating the Error Variance
2-6. Regression through the Origin and Regression on a Constant
Summary
Key Terms
Problems
Computer Exercises
Appendix 2A.
Chapter 3. Multiple Regression Analysis: Estimation
3-1. Motivation for Multiple Regression
3-1a. The Model with Two Independent Variables
3-1b. The Model with k Independent Variables
3-2. Mechanics and Interpretation of Ordinary Least Squares
3-2a. Obtaining the OLS Estimates
3-2b. Interpreting the OLS Regression Equation
3-2c. On the Meaning of “Holding Other Factors Fixed” in Multiple Regression
3-2d. Changing More than One Independent Variable Simultaneously
3-2e. OLS Fitted Values and Residuals
3-2f. A “Partialling Out” Interpretation of Multiple Regression
3-2g. Comparison of Simple and Multiple Regression Estimates
3-2h. Goodness-of-Fit
3-2i. Regression through the Origin
3-3. The Expected Value of the OLS Estimators
3-3a. Including Irrelevant Variables in a Regression Model
3-3b. Omitted Variable Bias: The Simple Case
3-3c. Omitted Variable Bias: More General Cases
3-4. The Variance of the OLS Estimators
3-4a. The Components of the OLS Variances. Multicollinearity
3-4b. Variances in Misspecified Models
3-4c. Estimating σ 2 : Standard Errors 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.
Chapter 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
4-2a. Testing against One-Sided Alternatives
4-2b. Two-Sided Alternatives
4-2c. Testing Other Hypotheses about β j
4-2d. Computing p-Values for t Tests
4-2e. A Reminder on the Language of Classical Hypothesis Testing
4-2f. Economic, or Practical, versus Statistical Significance
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
4-5a. Testing Exclusion Restrictions
4-5b. Relationship between F and t Statistics
4-5c. The R-Squared Form of the F Statistic
4-5d. Computing p-Values for F Tests
4-5e. The F Statistic for Overall Significance of a Regression
4-5f. Testing General Linear Restrictions
4-6. Reporting Regression Results
Summary
Key Terms
Problems
Computer Exercises
Chapter 5. Multiple Regression Analysis: OLS Asymptotics
5-1. Consistency
5-1a. Deriving the Inconsistency in OLS
5-2. Asymptotic Normality and Large Sample Inference
5-2a. Other Large Sample Tests: The Lagrange Multiplier Statistic
5-3. Asymptotic Efficiency of OLS
Summary
Key Terms
Problems
Computer Exercises
Appendix 5A.
Chapter 6. Multiple Regression Analysis: Further Issues
6-1. Effects of Data Scaling on OLS Statistics
6-1a. Beta Coefficients
6-2. More on Functional Form
6-2a. More on Using Logarithmic Functional Forms
6-2b. Models with Quadratics
6-2c. Models with Interaction Terms
6-2d. Computing Average Partial Effects
6-3. More on Goodness-of-Fit and Selection of Regressors
6-3a. Adjusted R-Squared
6-3b. Using Adjusted R-Squared to Choose between Nonnested Models
6-3c. Controlling for Too Many Factors in Regression Analysis
6-3d. Adding Regressors to Reduce the Error Variance
6-4. Prediction and Residual Analysis
6-4a. Confidence Intervals for Predictions
6-4b. Residual Analysis
6-4c. Predicting y When log ( y ) Is the Dependent Variable
6-4d. Predicting y When the Dependent Variable Is log ( y )
Summary
Key Terms
Problems
Computer Exercises
Appendix 6A.
Chapter 7. Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables
7-1. Describing Qualitative Information
7-2. A Single Dummy Independent Variable
7-2a. Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log ( y )
7-3. Using Dummy Variables for Multiple Categories
7-3a. Incorporating Ordinal Information by Using Dummy Variables
7-4. Interactions Involving Dummy Variables
7-4a. Interactions among Dummy Variables
7-4b. Allowing for Different Slopes
7-4c. Testing for Differences in Regression Functions across Groups
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
Chapter 8. Heteroskedasticity
8-1. Consequences of Heteroskedasticity for OLS
8-2. Heteroskedasticity-Robust Inference after OLS Estimation
8-2a. Computing Heteroskedasticity-Robust LM Tests
8-3. Testing for Heteroskedasticity
8-3a. The White Test for Heteroskedasticity
8-4. Weighted Least Squares Estimation
8-4a. The Heteroskedasticity Is Known up to a Multiplicative Constant
8-4b. The Heteroskedasticity Function Must Be Estimated: Feasible GLS
8-4c. What If the Assumed Heteroskedasticity Function Is Wrong?
8-4d. Prediction and Prediction Intervals with Heteroskedasticity
8-5. The Linear Probability Model Revisited
Summary
Key Terms
Problems
Computer Exercises
Chapter 9. More on Specification and Data Issues
9-1. Functional Form Misspecification
9-1a. RESET as a General Test for Functional Form Misspecification
9-1b. Tests against Nonnested Alternatives
9-2. Using Proxy Variables for Unobserved Explanatory Variables
9-2a. Using Lagged Dependent Variables as Proxy Variables
9-2b. A Different Slant on Multiple Regression
9-3. Models with Random Slopes
9-4. Properties of OLS under Measurement Error
9-4a. Measurement Error in the Dependent Variable
9-4b. Measurement Error in an Explanatory Variable
9-5. Missing Data, Nonrandom Samples, and Outlying Observations
9-5a. Missing Data
9-5b. Nonrandom
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