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• About the Author
• Contents at a Glance
• Table of Contents
• Introduction
• About This Book
• Foolish Assumptions
• Icons Used in This Book
• Beyond the Book
• Where to Go from Here
• Part I: Getting Started with Econometrics
• Chapter 1: Econometrics: The Economist’s Approach to Statistical Analysis
• Evaluating Economic Relationships
• Applying Statistical Methods to Economic Problems
• Using Econometric Software: An Introduction to STATA
• Chapter 2: Getting the Hang of Probability
• Reviewing Random Variables and Probability Distributions
• Understanding Summary Characteristics of Random Variables
• Chapter 3: Making Inferences and Testing Hypotheses
• Getting to Know Your Data with Descriptive Statistics
• Laying the Groundwork of Prediction with the Normal and Standard Normal Distributions
• Working with Parts of the Population: Sampling Distributions
• Making Inferences and Testing Hypotheses with Probability Distributions
• Part II: Building the Classical Linear Regression Model
• Chapter 4: Understanding the Objectives of Regression Analysis
• Making a Case for Causality
• Getting Acquainted with the Population Regression Function (PRF)
• Collecting and Organizing Data for Regression Analysis
• Chapter 5: Going Beyond Ordinary with the Ordinary Least Squares Technique
• Defining and Justifying the Least Squares Principle
• Estimating the Regression Function and the Residuals
• Obtaining Estimates of the Regression Parameters
• Interpreting Regression Coefficients
• Measuring Goodness of Fit
• Chapter 6: Assumptions of OLS Estimation and the Gauss-Markov Theorem
• Characterizing the OLS Assumptions
• Relying on the CLRM Assumptions: The Gauss-Markov Theorem
• Chapter 7: The Normality Assumption and Inference with OLS
• Describing the Role of the Normality Assumption
• Testing the Significance of Individual Regression Coefficients
• Analyzing Variance to Determine Overall or Joint Significance
• Applying Forecast Error to OLS Predictions
• Part III: Working with the Classical Regression Model
• Chapter 8: Functional Form, Specification, and Structural Stability
• Employing Alternative Functions
• Giving Linearity to Nonlinear Models
• Checking for Misspecification
• Chapter 9: Regression with Dummy Explanatory Variables
• Numbers Please! Quantifying Qualitative Information
• Finding Average Differences by Using a Dummy Variable
• Combining Quantitative and Qualitative Data in the Regression Model
• Interacting Quantitative and Qualitative Variables
• Interacting Two (or More) Qualitative Characteristics
• Segregate and Integrate: Testing for Significance
• Part IV: Violations of Classical Regression Model Assumptions
• Chapter 10: Multicollinearity
• Distinguishing between the Types of Multicollinearity
• Rules of Thumb for Identifying Multicollinearity
• Knowing When and How to Resolve Multicollinearity Issues
• Chapter 11: Heteroskedasticity
• Distinguishing between Homoskedastic and Heteroskedastic Disturbances
• Detecting Heteroskedasticity with Residual Analysis
• Correcting Your Regression Model for the Presence of Heteroskedasticity
• Chapter 12: Autocorrelation
• Examining Patterns of Autocorrelation
• Illustrating the Effect of Autoregressive Errors
• Analyzing Residuals to Test for Autocorrelation
• Remedying Harmful Autocorrelation
• Part V: Discrete and Restricted Dependent Variables in Econometrics
• Chapter 13: Qualitative Dependent Variables
• Modeling Discrete Outcomes with the Linear Probability Model (LPM)
• Presenting the Three Main LPM Problems
• Specifying Appropriate Nonlinear Functions: The Probit and Logit Models
• Using Maximum Likelihood (ML) Estimation
• Interpreting Probit and Logit Estimates
• Chapter 14: Limited Dependent Variable Models
• The Nitty-Gritty of Limited Dependent Variables
• Modifying Regression Analysis for Limited Dependent Variables
• Part VI: Extending the Basic Econometric Model
• Chapter 15: Static and Dynamic Models
• Using Contemporaneous and Lagged Variables in Regression Analysis
• Projecting Time Trends with OLS
• Using OLS for Seasonal Adjustments
• Chapter 16: Diving into Pooled Cross-Section Analysis
• Adding a Dynamic Time Element to the Mix
• Using Experiments to Estimate Policy Effects with Pooled Cross Sections
• Chapter 17: Panel Econometrics
• Estimating the Uniqueness of Each Individual Unit
• Increasing the Efficiency of Estimation with Random Effects
• Testing Efficiency against Consistency with the Hausman Test
• Part VII: The Part of Tens
• Chapter 18: Ten Components of a Good Econometrics Research Project
• Introducing Your Topic and Posing the Primary Question of Interest
• Discussing the Relevance and Importance of Your Topic
• Reviewing the Existing Literature
• Describing the Conceptual or Theoretical Framework
• Explaining Your Econometric Model
• Discussing the Estimation Method(s)
• Providing a Detailed Description of Your Data
• Constructing Tables and Graphs to Display Your Results
• Interpreting the Reported Results
• Summarizing What You Learned
• Chapter 19: Ten Common Mistakes in Applied Econometrics
• Failing to Use Your Common Sense and Knowledge of Economic Theory
• Asking the Wrong Questions First
• Ignoring the Work and Contributions of Others
• Failing to Familiarize Yourself with the Data
• Making It Too Complicated
• Being Inflexible to Real-World Complications
• Looking the Other Way When You See Bizarre Results
• Obsessing over Measures of Fit and Statistical Significance
• Forgetting about Economic Significance
• Assuming Your Results Are Robust
• Appendix: Statistical Tables
• The Standard Normal Distribution
• t-Distribution
• Chi-Squared Distribution
• F-Distribution
• Durbin-Watson d-Statistic
• Index