Introductory Econometrics: A Modern Approach, Sixth Edition 

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Comment from the Stata technical groupThe sixth edition of Jeffrey Wooldridge's textbook, Introductory Econometrics: A Modern Approach, lives up to its subtitle in its choice of topics and its treatment of standard material. Wooldridge recognizes that modern econometrics involves much more than ordinary least squares (OLS) with a few extensions to handle the special cases commonly encountered in econometric data. In addition to chapters on OLS, he includes chapters on current techniques of estimation and inference for timeseries data, panel data, limited dependent variables, and sample selection. In his treatments of OLS and twostage least squares, Wooldridge breaks new ground by concentrating on advanced statistical concepts instead of matrix algebra. A traditional approach to introductory econometrics would use advanced sections to explain matrix algebra and its applications in econometrics. In contrast, Wooldridge uses the advanced sections of his text to introduce recently developed statistical concepts and techniques. This approach leads to a text with greater breadth than is usual in books of this type. This book is equally useful for advanced undergraduate study, as the basis of a survey course at the graduate level, or as a conceptual supplement to advanced courses. The sixth edition provides an excellent introduction to average partial effects (APEs), which are now the effect measure estimated in much empirical work. This new edition also contains an introduction to the modern approach to missing data, a better discussion of Rsquare comparison with missing data, and a slew of new exercises. The result is that an excellent introductory book has been made even better. 

Table of contentsView table of contents >> Preface
About the Author
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
CrossSectional Data
1.4 Causality and the Notion of Ceteris Paribus in Econometric Analysis Time Series Data Pooled Cross Sections Panel or Longitudinal Data A Comment on Data Structures Summary Key Terms Problems Computer Exercises PART 1 Regression Analysis with CrossSectional Data
Chapter 2 The Simple Regression Model
2.1 Definition of the Simple Regression Model
2.2 Deriving the Ordinary Least Squares Estimates
A Note on Terminology
2.3 Properties of OLS on Any Sample of Data
Fitted Values and Residuals
2.4 Units of Measurement and Functional Form
Algebraic Properties of OLS Statistics GoodnessofFit
The Effects of Changing Units of Measurement on OLS Statistics
2.5 Expected Values and Variances of the OLS Estimators
Incorporating Nonlinearities in Simple Regression The Meaning of "Linear" Regression
Unbiasedness of OLS
2.6 Regression through the Origin and Regression on a Constant Variances of the OLS Estimators Estimating the Error Variance Summary Key Terms Problems Computer Exercises Appendix 2A Chapter 3 Multiple Regression Analysis: Estimation
3.1 Motivation for Multiple Regression
The Model with Two Independent Variables
3.2 Mechanics and Interpretation of Ordinary Least Squares
The Model with k Independent Variables
Obtaining the OLS Estimates
3.3 The Expected Value of the OLS Estimators
Interpreting the OLS Regression Equation On the Meaning of "Holding Other Factors Fixed" in Multiple Regression Changing More Than One Independent Variable Simultaneously OLS Fitted Values and Residuals A "Partialling Out" Interpretation of Multiple Regression Comparison of Simple and Multiple Regression Estimates GoodnessofFit Regression through the Origin
Including Irrelevant Variables in a Regression Model
3.4 The Variance of the OLS Estimators
Omitted Variable Bias: The Simple Case Omitted Variable Bias: More General Cases
The Components of the OLS Variances
3.5 Efficiency of OLS: The GaussMarkov Theorem Multicollinearity Variances in Misspecified Models Estimating σ^{2} Standard Errors of the OLS Estimators 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
Testing against OneSided Alternatives
4.3 Confidence Intervals TwoSided Alternatives Testing Other Hypotheses about β_{j} Computing pValues for t Tests A Reminder on the Language of Classical Hypothesis Testing Economic, or Practical, versus Statistical Significance 4.4 Testing Hypotheses about a Single Linear Combination of the Parameters 4.5 Testing Multiple Linear Restrictions: The F Test
Testing Exclusion Restrictions
4.6 Reporting Regression Results Relationship between F and t Statistics The RSquared Form of the F Statistic Computing pValues for F Tests The F Statistic for Overall Significance of a Regression Testing General Linear Restrictions Summary Key Terms Problems Computer Exercises Chapter 5 Multiple Regression Analysis: OLS Asymptotics
5.1 Consistency
Deriving the Inconsistency in OLS
5.2 Asymptotic Normality and Large Sample Inference
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
Beta Coefficients
6.2 More on Functional Form
More on Using Logarithmic Functional Forms
6.3 More on GoodnessofFit and Selection of Regressors
Models with Quadratics Models with Interaction Terms Computing Average Partial Effects
Adjusted RSquared
6.4 Prediction and Residual Analysis
Using Adjusted RSquared to Choose between Nonnested Models Controlling for Too Many Factors in Regression Analysis Adding Regressors to Reduce the Error Variance
Confidence Intervals for Predictions
Summary Residual Analysis Predicting y When log(y) Is the Dependent Variable Predicting y When the Dependent Variable Is log(y): 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
Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log(y)
7.3 Using Dummy Variables for Multiple Categories
Incorporating Ordinal Information by Using Dummy Variables
7.4 Interactions Involving Dummy Variables
Interactions among Dummy Variables
7.5 A Binary Dependent Variable: The Linear Probability Model Allowing for Different Slopes Testing for Differences in Regression Functions across Groups 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 HeteroskedasticityRobust Inference after OLS Estimation
Computing HeteroskedasticityRobust LM Tests
8.3 Testing for Heteroskedasticity
The White Test for Heteroskedasticity
8.4 Weighted Least Squares Estimation
The Heteroskedasticity Is Known up to a Multiplicative Constant
8.5 The Linear Probability Model Revisited The Heteroskedasticity Function Must Be Estimated: Feasible GLS What If the Assumed Heteroskedasticity Function Is Wrong? Prediction and Prediction Intervals with Heteroskedasticity Summary Key Terms Problems Computer Exercises Chapter 9 More on Specification and Data Issues
9.1 Functional Form Misspecification
RESET as a General Test for Functional Form Misspecification
9.2 Using Proxy Variables for Unobserved Explanatory Variables
Tests against Nonnested Alternatives
Using Lagged Dependent Variables as Proxy Variables
9.3 Models with Random Slopes A Different Slant on Multiple Regression 9.4 Properties of OLS under Measurement Error
Measurement Error in the Dependent Variable
9.5 Missing Data, Nonrandom Samples, and Outlying Observations
Measurement Error in an Explanatory Variable
Missing Data
9.6 Least Absolute Deviations Estimation Nonrandom Samples Outliers and Influential Observations 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
Static Models
10.3 Finite Sample Properties of OLS under Classical Assumptions
Finite Distributed Lag Models A Convention about the Time Index
Unbiasedness of OLS
10.4 Functional Form, Dummy Variables, and Index Numbers The Variances of the OLS Estimators and the GaussMarkov Theorem Inference under the Classical Linear Model Assumptions 10.5 Trends and Seasonality
Characterizing Trending Time Series
Summary Using Trending Variables in Regression Analysis A Detrending Interpretation of Regressions with a Time Trend Computing RSquared When the Dependent Variable Is Trending Seasonality Key Terms Problems Computer Exercises Chapter 11 Further Issues in Using OLS with Time Series Data
11.1 Stationary and Weakly Dependent Time Series
Stationary and Nonstationary Time Series
11.2 Asymptotic Properties of OLS Weakly Dependent Time Series 11.3 Using Highly Persistent Time Series in Regression Analysis
Highly Persistent Time Series
11.4 Dynamically Complete Models and the Absence of Serial Correlation Transformations on Highly Persistent Time Series Deciding Whether a Time Series Is I(1) 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
Unbiasedness and Consistency
12.2 Testing for Serial Correlation Efficiency and Inference GoodnessofFit Serial Correlation in the Presence of Lagged Dependent Variables A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors The DurbinWatson Test under Classical Assumptions Testing for AR(1) Serial Correlation without Strictly Exogenous Regressors Testing for Higher Order Serial Correlation 12.3 Correcting for Serial Correlation with Strictly Exogenous Regressors
Obtaining the Best Linear Unbiased Estimator in the AR(1) Model
12.4 Differencing and Serial Correlation Feasible GLS Estimation with AR(1) Errors Comparing OLS and FGLS Correcting for Higher Order Serial Correlation 12.5 Serial CorrelationRobust Inference after OLS 12.6 Heteroskedasticity in Time Series Regressions
HeteroskedasticityRobust Statistics
Summary Testing for Heteroskedasticity Autoregressive Conditional Heteroskedasticity Heteroskedasticity and Serial Correlation in Regression Models 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
The Chow Test for Structural Change across Time
13.2 Policy Analysis with Pooled Cross Sections 13.3 TwoPeriod Panel Data Analysis
Organizing Panel Data
13.4 Policy Analysis with TwoPeriod Panel Data 13.5 Differencing with More Than Two Time Periods
Potential Pitfalls in First Differencing Panel Data
Summary Key Terms Problems Computer Exercises Appendix 13A Chapter 14 Advanced Panel Data Methods
14.1 Fixed Effects Estimation
The Dummy Variable Regression
14.2 Random Effects Models
Fixed Effects or First Differencing? Fixed Effects with Unbalanced Panels
Random Effects or Fixed Effects?
14.3 The Correlated Random Effects Approach
Unbalanced Panels
14.4 Applying Panel Data Methods to Other Data Structures Summary Key Terms Problems Computer Exercises Appendix 14A Chapter 15 Instrumental Variables Estimation and Two Stage Least Squares
15.1 Motivation: Omitted Variables in a Simple Regression Model
Statistical Inference with the IV Estimator
15.2 IV Estimation of the Multiple Regression Model Properties of IV with a Poor Instrumental Variable Computing RSquared after IV Estimation 15.3 Two Stage Least Squares
A Single Endogenous Explanatory Variable
15.4 IV Solutions to ErrorsinVariables Problems Multicollinearity and 2SLS Detecting Weak Instruments Multiple Endogenous Explanatory Variables Testing Multiple Hypotheses after 2SLS Estimation 15.5 Testing for Endogeneity and Testing Overidentifying Restrictions
Testing for Endogeneity
15.6 2SLS with Heteroskedasticity Testing Overidentification Restrictions 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 Appendix 15A 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
Identification in a TwoEquation System
16.4 Systems with More Than Two Equations
Estimation by 2SLS
Identification in Systems with Three or More Equations
16.5 Simultaneous Equations Models with Time Series Estimation 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
Specifying Logit and Probit Models
17.2 The Tobit Model for Corner Solution Responses
Maximum Likelihood Estimation of Logit and Probit Models Testing Multiple Hypotheses Interpreting the Logit and Probit Estimates
Interpreting the Tobit Estimates
17.3 The Poisson Regression Model Specification Issues in Tobit Models 17.4 Censored and Truncated Regression Models
Censored Regression Models
17.5 Sample Selection Corrections
Truncated Regression Models
When Is OLS on the Selected Sample Consistent?
Summary Incidental Truncation Key Terms Problems Computer Exercises Appendix 17A Appendix 17B Chapter 18 Advanced Time Series Topics
18.1 Infinite Distributed Lag Models
The Geometric (or Koyck) Distributed Lag
18.2 Testing for Unit Roots Rational Distributed Lag Models 18.3 Spurious Regression 18.4 Cointegration and Error Correction Models
Cointegration
18.5 Forecasting
Error Correction Models
Types of Regression Models Used for Forecasting
Summary OneStepAhead Forecasting Comparing OneStepAhead Forecasts MultipleStepAhead Forecasts Forecasting Trending, Seasonal, and Integrated Processes 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
Deciding on the Appropriate Data Set
19.4 Econometric Analysis Entering and Storing Your Data Inspecting, Cleaning, and Summarizing Your Data 19.5 Writing an Empirical Paper
Introduction
Summary Conceptual (or Theoretical) Framework Econometric Models and Estimation Methods The Data Results Conclusions Style Hints Key Terms Sample Empirical Projects List of Journals Data Sources APPENDIX 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
Quadratic Functions
A.5 Differential Calculus The Natural Logarithm The Exponential Function Summary Key Terms Problems APPENDIX B Fundamentals of Probability
B.1 Random Variables and Their Probability Distributions
Discrete Random Variables
B.2 Joint Distributions, Conditional Distributions, and Independence
Continuous Random Variables
Joint Distributions and Independence
B.3 Features of Probability Distributions
Conditional Distributions
A Measure of Central Tendency: The Expected Value
B.4 Features of Joint and Conditional Distributions
Properties of Expected Values Another Measure of Central Tendency: The Median Measures of Variability: Variance and Standard Deviation Variance Standard Deviation Standardizing a Random Variable Skewness and Kurtosis
Measures of Association: Covariance and Correlation
B.5 The Normal and Related Distributions
Covariance Correlation Coefficient Variance of Sums of Random Variables Conditional Expectation Properties of Conditional Expectation Conditional Variance
The Normal Distribution
Summary The Standard Normal Distribution Additional Properties of the Normal Distribution The ChiSquare Distribution The t Distribution The F Distribution Key Terms Problems APPENDIX C Fundamentals of Mathematical Statistics
C.1 Populations, Parameters, and Random Sampling
Sampling
C.2 Finite Sample Properties of Estimators
Estimators and Estimates
C.3 Asymptotic or Large Sample Properties of Estimators
Unbiasedness The Sampling Variance of Estimators Efficiency
Consistency
C.4 General Approaches to Parameter Estimation
Asymptotic Normality
Method of Moments
C.5 Interval Estimation and Confidence Intervals
Maximum Likelihood Least Squares
The Nature of Interval Estimation
C.6 Hypothesis Testing
Confidence Intervals for the Mean from a Normally Distributed Population A Simple Rule of Thumb for a 95% Confidence Interval Asymptotic Confidence Intervals for Nonnormal Populations
Fundamentals of Hypothesis Testing
C.7 Remarks on Notation Testing Hypotheses about the Mean in a Normal Population Asymptotic Tests for Nonnormal Populations Computing and Using pValues The Relationship between Confidence Intervals and Hypothesis Testing Practical versus Statistical Significance Summary Key Terms Problems APPENDIX D Summary of Matrix Algebra
D.1 Basic Definitions
D.2 Matrix Operations
Matrix Addition
D.3 Linear Independence and Rank of a Matrix Scalar Multiplication Matrix Multiplication Transpose Partitioned Matrix Multiplication Trace Inverse 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
Expected Value
Summary VarianceCovariance Matrix Multivariate Normal Distribution ChiSquare Distribution t Distribution F Distribution Key Terms Problems APPENDIX E The Linear Regression Model in Matrix Form
E.1 The Model and Ordinary Least Squares Estimation
The FrischWaugh Theorem
E.2 Finite Sample Properties of OLS E.3 Statistical Inference E.4 Some Asymptotic Analysis
Wald Statistics for Testing Multiple Hypotheses
Summary Key Terms Problems APPENDIX F Answers to Chapter Questions
APPENDIX G Statistical Tables
References
Glossary Index 