Efnisyfirlit

• 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

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