Efnisyfirlit

• Brief Contents
• 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, Ceteris Paribus, and Counterfactual Reasoning
  • 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
    • 2-7 Regression on a Binary Explanatory Variable
      • 2-7a Counterfactual Outcomes, Causality, and Policy Analysis
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • 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 s2: 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
    • 3-7 Several Scenarios for Applying Multiple Regression
      • 3-7a Prediction
      • 3-7b Efficient Markets
      • 3-7c Measuring the Tradeoff between Two Variables
      • 3-7d Testing for Ceteris Paribus Group Differences
      • 3-7e Potential Outcomes, Treatment Effects, and Policy Analysis
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • 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 bj
      • 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
    • 4-7 Revisiting Causal Effects and Policy Analysis
    • 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
  • 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
      • 6.4 a 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
  • Chapter 7: Multiple Regression Analysis with Qualitative Information
    • 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
      • 7-6a Program Evaluation and Unrestricted Regression Adjustment
    • 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
      • 9-2c Potential Outcomes and Proxy Variables
    • 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 Samples
      • 9-5c Outliers and Influential Observations
    • 9-6 Least Absolute Deviations Estimation
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
• Part 2: Regression Analysis with Time Series Data
  • Chapter 10: Basic Regression Analysis with Time Series Data
    • 10-1 The Nature of Time Series Data
    • 10-2 Examples of Time Series Regression Models
      • 10-2a Static Models
      • 10-2b Finite Distributed Lag Models
      • 10-2c A Convention about the Time Index
    • 10-3 Finite Sample Properties of OLS under Classical Assumptions
      • 10-3a Unbiasedness of OLS
      • 10-3b The Variances of the OLS Estimators and the Gauss-Markov Theorem
      • 10-3c Inference under the Classical Linear Model Assumptions
    • 10-4 Functional Form, Dummy Variables, and Index Numbers
    • 10-5 Trends and Seasonality
      • 10-5a Characterizing Trending Time Series
      • 10-5b Using Trending Variables in Regression Analysis
      • 10-5c A Detrending Interpretation of Regressions with a Time Trend
      • 10-5d Computing R-Squared When the Dependent Variable Is Trending
      • 10-5e Seasonality
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • Chapter 11: Further Issues in Using OLS with Time Series Data
    • 11-1 Stationary and Weakly Dependent Time Series
      • 11-1a Stationary and Nonstationary Time Series
      • 11-1b Weakly Dependent Time Series
    • 11-2 Asymptotic Properties of OLS
    • 11-3 Using Highly Persistent Time Series in Regression Analysis
      • 11-3a Highly Persistent Time Series
      • 11-3b Transformations on Highly Persistent Time Series
      • 11-3c Deciding Whether a Time Series Is I(1)
    • 11-4 Dynamically Complete Models and the Absence of Serial Correlation
    • 11-5 The Homoskedasticity Assumption for Time Series Models
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • Chapter 12: Serial Correlation and Heteroskedasticity in Time Series Regressions
    • 12-1 Properties of OLS with Serially Correlated Errors
      • 12-1a Unbiasedness and Consistency
      • 12-1b Efficiency and Inference
      • 12-1c Goodness-of-Fit
      • 12-1d Serial Correlation in the Presence of Lagged Dependent Variables
    • 12-2 Serial Correlation–Robust Inference after OLS
    • 12-3 Testing for Serial Correlation
      • 12-3a A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors
      • 12-3b The Durbin-Watson Test under Classical Assumptions
      • 12-3c Testing for AR(1) Serial Correlation without Strictly Exogenous Regressors
      • 12-3d Testing for Higher-Order Serial Correlation
    • 12-4 Correcting for Serial Correlation with Strictly Exogenous Regressors
      • 12-4a Obtaining the Best Linear Unbiased Estimator in the AR(1) Model
      • 12-4b Feasible GLS Estimation with AR(1) Errors
      • 12-4c Comparing OLS and FGLS
      • 12-4d Correcting for Higher-Order Serial Correlation
      • 12-4e What if the Serial Correlation Model Is Wrong?
    • 12-5 Differencing and Serial Correlation
    • 12-6 Heteroskedasticity in Time Series Regressions
      • 12-6a Heteroskedasticity-Robust Statistics
      • 12-6b Testing for Heteroskedasticity
      • 12-6c Autoregressive Conditional Heteroskedasticity
      • 12-6d Heteroskedasticity and Serial Correlation in Regression Models
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
• Part 3: Advanced Topics
  • Chapter 13: Pooling Cross Sections across Time: Simple Panel Data Methods
    • 13-1 Pooling Independent Cross Sections across Time
      • 13-1a The Chow Test for Structural Change across Time
    • 13-2 Policy Analysis with Pooled Cross Sections
      • 13-2a Adding an Additional Control Group
      • 13-2b A General Framework for Policy Analysis with Pooled Cross Sections
    • 13-3 Two-Period Panel Data Analysis
      • 13-3a Organizing Panel Data
    • 13-4 Policy Analysis with Two-Period Panel Data
    • 13-5 Differencing with More Than Two Time Periods
      • 13-5a Potential Pitfalls in First Differencing Panel Data
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • Chapter 14: Advanced Panel Data Methods
    • 14-1 Fixed Effects Estimation
      • 14-1a The Dummy Variable Regression
      • 14-1b Fixed Effects or First Differencing?
      • 14-1c Fixed Effects with Unbalanced Panels
    • 14-2 Random Effects Models
      • 14-2a Random Effects or Pooled OLS?
      • 14-2b Random Effects or Fixed Effects?
    • 14-3 The Correlated Random Effects Approach
      • 14-3a Unbalanced Panels
    • 14-4 General Policy Analysis with Panel Data
      • 14-4a Advanced Considerations with Policy Analysis
    • 14-5 Applying Panel Data Methods to Other Data Structures
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • Chapter 15: Instrumental Variables Estimation and Two-Stage Least Squares
    • 15-1 Motivation: Omitted Variables in a Simple Regression Model
      • 15-1a Statistical Inference with the IV Estimator
      • 15-1b Properties of IV with a Poor Instrumental Variable
      • 15-1c Computing R-Squared after IV Estimation
    • 15-2 IV Estimation of the Multiple Regression Model
    • 15-3 Two-Stage Least Squares
      • 15-3a A Single Endogenous Explanatory Variable
      • 15-3b Multicollinearity and 2SLS
      • 15-3c Detecting Weak Instruments
      • 15-3d Multiple Endogenous Explanatory Variables
      • 15-3e Testing Multiple Hypotheses after 2SLS Estimation
    • 15-4 IV Solutions to Errors-in-Variables Problems
    • 15-5 Testing for Endogeneity and Testing Overidentifying Restrictions
      • 15-5a Testing for Endogeneity
      • 15-5b Testing Overidentification Restrictions
    • 15-6 2SLS with Heteroskedasticity
    • 15-7 Applying 2SLS to Time Series Equations
    • 15-8 Applying 2SLS to Pooled Cross Sections and Panel Data
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • Chapter 16: Simultaneous Equations Models
    • 16-1 The Nature of Simultaneous Equations Models
    • 16-2 Simultaneity Bias in OLS
    • 16-3 Identifying and Estimating a Structural Equation
      • 16-3a Identification in a Two-Equation System
      • 16-3b Estimation by 2SLS
    • 16-4 Systems with More Than Two Equations
      • 16-4a Identification in Systems with Three or More Equations
      • 16-4b Estimation
    • 16-5 Simultaneous Equations Models with Time Series
    • 16-6 Simultaneous Equations Models with Panel Data
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • Chapter 17: Limited Dependent Variable Models and Sample Selection Corrections
    • 17-1 Logit and Probit Models for Binary Response
      • 17-1a Specifying Logit and Probit Models
      • 17-1b Maximum Likelihood Estimation of Logit and Probit Models
      • 17-1c Testing Multiple Hypotheses
      • 17-1d Interpreting the Logit and Probit Estimates
    • 17-2 The Tobit Model for Corner Solution Responses
      • 17-2a Interpreting the Tobit Estimates
      • 17-2b Specification Issues in Tobit Models
    • 17-3 The Poisson Regression Model
    • 17-4 Censored and Truncated Regression Models
      • 17-4a Censored Regression Models
      • 17-4b Truncated Regression Models
    • 17-5 Sample Selection Corrections
      • 17-5a When Is OLS on the Selected Sample Consistent?
      • 17-5b Incidental Truncation
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • Chapter 18: Advanced Time Series Topics
    • 18-1 Infinite Distributed Lag Models
      • 18-1a The Geometric (or Koyck) Distributed Lag Model
      • 18-1b Rational Distributed Lag Models
    • 18-2 Testing for Unit Roots
    • 18-3 Spurious Regression
    • 18-4 Cointegration and Error Correction Models
      • 18-4a Cointegration
      • 18-4b Error Correction Models
    • 18-5 Forecasting
      • 18-5a Types of Regression Models Used for Forecasting
      • 18-5b One-Step-Ahead Forecasting
      • 18-5c Comparing One-Step-Ahead Forecasts
      • 18-5d Multiple-Step-Ahead Forecasts
      • 18-5e Forecasting Trending, Seasonal, and Integrated Processes
    • Summary
    • Key Terms
    • Problems
    • Computer Exercises
  • Chapter 19: Carrying Out an Empirical Project
    • 19-1 Posing a Question
    • 19-2 Literature Review
    • 19-3 Data Collection
      • 19-3a Deciding on the Appropriate Data Set
      • 19-3b Entering and Storing Your Data
      • 19-3c Inspecting, Cleaning, and Summarizing Your Data
    • 19-4 Econometric Analysis
    • 19-5 Writing an Empirical Paper
      • 19-5a Introduction
      • 19-5b Conceptual (or Theoretical) Framework
      • 19-5c Econometric Models and Estimation Methods
      • 19-5d The Data
      • 19-5e Results
      • 19.5f Conclusions
      • 19-5g Style Hints
    • Summary
    • Key Terms
    • Sample Empirical Projects
    • List of Journals
    • Data Sources
• Math Refresher A Basic Mathematical Tools
  • A-1 The Summation Operator and Descriptive Statistics
  • A-2 Properties of Linear Functions
  • A-3 Proportions and Percentages
  • A-4 Some Special Functions and Their Properties
    • A-4a Quadratic Functions
    • A-4b The Natural Logarithm
    • A-4c The Exponential Function
  • A-5 Differential Calculus
    • Summary
    • Key Terms
    • Problems
• Math Refresher B Fundamentals of Probability
  • B-1 Random Variables and Their Probability Distributions
    • B-1a Discrete Random Variables
    • B-1b Continuous Random Variables
  • B-2 Joint Distributions, Conditional Distributions, and Independence
    • B-2a Joint Distributions and Independence
    • B-2b Conditional Distributions
  • B-3 Features of Probability Distributions
    • B-3a A Measure of Central Tendency: The Expected Value
    • B-3b Properties of Expected Values
    • B-3c Another Measure of Central Tendency: The Median
    • B-3d Measures of Variability: Variance and Standard Deviation
    • B-3e Variance
    • B-3f Standard Deviation
    • B-3g Standardizing a Random Variable
    • B-3h Skewness and Kurtosis
  • B-4 Features of Joint and Conditional Distributions
    • B-4a Measures of Association: Covariance and Correlation
    • B-4b Covariance
    • B-4c Correlation Coefficient
    • B-4d Variance of Sums of Random Variables
    • B-4e Conditional Expectation
    • B-4f Properties of Conditional Expectation
    • B-4g Conditional Variance
  • B-5 The Normal and Related Distributions
    • B-5a The Normal Distribution
    • B-5b The Standard Normal Distribution
    • B-5c Additional Properties of the Normal Distribution
    • B-5d The Chi-Square Distribution
    • B-5e The t Distribution
    • B-5f The F Distribution
  • Summary
  • Key Terms
  • Problems
• Math Refresher C Fundamentals of Mathematical Statistics
  • C-1 Populations, Parameters, and Random Sampling
    • C-1a Sampling
  • C-2 Finite Sample Properties of Estimators
    • C-2a Estimators and Estimates
    • C-2b Unbiasedness
    • C-2c The Sampling Variance of Estimators
    • C-2d Efficiency
  • C-3 Asymptotic or Large Sample Properties of Estimators
    • C-3a Consistency
    • C-3b Asymptotic Normality
  • C-4 General Approaches to Parameter Estimation
    • C-4a Method of Moments
    • C-4b Maximum Likelihood
    • C-4c Least Squares
  • C-5 Interval Estimation and Confidence Intervals
    • C-5a The Nature of Interval Estimation
    • C-5b Confidence Intervals for the Mean from a Normally Distributed Population
    • C-5c A Simple Rule of Thumb for a 95% Confidence Interval
    • C-5d Asymptotic Confidence Intervals for Nonnormal Populations
  • C-6 Hypothesis Testing
    • C-6a Fundamentals of Hypothesis Testing
    • C-6b Testing Hypotheses about the Mean in a Normal Population
    • C-6c Asymptotic Tests for Nonnormal Populations
    • C-6d Computing and Using p-Values
    • C-6e The Relationship between Confidence Intervals and Hypothesis Testing
    • C-6f Practical versus Statistical Significance
  • C-7 Remarks on Notation
  • Summary
  • Key Terms
  • Problems
• Advanced Treatment D Summary of Matrix Algebra
  • D-1 Basic Definitions
  • D-2 Matrix Operations
    • D-2a Matrix Addition
    • D-2b Scalar Multiplication
    • D-2c Matrix Multiplication
    • D-2d Transpose
    • D-2e Partitioned Matrix Multiplication
    • D-2f Trace
    • D-2g Inverse
  • D-3 Linear Independence and Rank of a Matrix
  • D-4 Quadratic Forms and Positive Definite Matrices
  • D-5 Idempotent Matrices
  • D-6 Differentiation of Linear and Quadratic Forms
  • D-7 Moments and Distributions of Random Vectors
    • D-7a Expected Value
    • D-7b Variance-Covariance Matrix
    • D-7c Multivariate Normal Distribution
    • D-7d Chi-Square Distribution
    • D-7e t Distribution
    • D-7f F Distribution
  • Summary
  • Key Terms
  • Problems
• Advanced Treatment E The Linear Regression Model in Matrix Form
  • E-1 The Model and Ordinary Least Squares Estimation
    • E-1a The Frisch-Waugh Theorem
  • E-2 Finite Sample Properties of OLS
  • E-3 Statistical Inference
  • E-4 Some Asymptotic Analysis
    • E-4a Wald Statistics for Testing Multiple Hypotheses
  • Summary
  • Key Terms
  • Problems
• Answers to Going Further Questions
• Statistical Tables
• References
• Glossary
• Index

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