Introductory Econometrics: A Modern Approach, Fifth Edition
Author: 
Jeffrey M. Wooldridge 
Publisher: 
SouthWestern (Cengage Learning) 
Copyright: 
2012 
ISBN13: 
9781111531041 
Pages: 
881; hardcover 
Price: 
$209.00 



Comment from the Stata technical group
The fifth 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 fifth edition contains a new section that highlights the differences
between a model and an estimator as well as expanded treatments of models for
proportional dependent variables, of using proxy variables to model
unobserved confounders, and of least absolutedeviations estimators. The
result is that an excellent introductory book has been made even better.
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
Time Series Data
Pooled Cross Sections
Panel or Longitudinal Data
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 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
Algebraic Properties of OLS Statistics
GoodnessofFit
2.4 Units of Measurement and Functional Form
The Effects of Changing Units of Measurement on OLS Statistics
Incorporating Nonlinearities in Simple Regression
The Meaning of “Linear” Regression
2.5 Expected Values and Variances of the OLS Estimators
Unbiasedness of OLS
Variances of the OLS Estimators
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
The Model with Two Independent Variables
The Model with k Independent Variables
3.2 Mechanics and Interpretation of Ordinary Least Squares
Obtaining the OLS Estimates
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
3.3 The Expected Value of the OLS Estimators
Including Irrelevant Variables in a Regression Model
Omitted Variable Bias: The Simple Case
Omitted Variable Bias: More General Cases
3.4 The Variance of the OLS Estimators:
The Components of the OLS Variances: Multicollinearity
Variances in Misspecified Models
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
Testing against OneSided Alternatives
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
4.3 Confidence Intervals
4.4 Testing Hypotheses about a Single Linear Combination of the Parameters
4.5 Testing Multiple Linear Restrictions: The
F Test
Testing Exclusion Restrictions
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
4.6 Reporting Regression Results
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
Models with Quadratics
Models with Interaction Terms
6.3 More on GoodnessofFit and Selection of Regressors
Adjusted RSquared
Using Adjusted RSquared to Choose between Nonnested Models
Controlling for Too Many Factors in Regression Analysis
Adding Regressors to Reduce the Error Variance
6.4 Prediction and Residual Analysis
Confidence Intervals for Predictions
Residual Analysis
Predicting y When log(y) Is the Dependent Variable
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
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
Allowing for Different Slopes
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 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
The Heteroskedasticity Function Must Be Estimated: Feasible GLS
What If the Assumed Heteroskedasticity Function Is Wrong?
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
RESET as a General Test for Functional Form Misspecification
Tests against Nonnested Alternatives
9.2 Using Proxy Variables for Unobserved Explanatory Variables
Using Lagged Dependent Variables as Proxy Variables
A Different Slant on Multiple Regression
9.3 Models with Random Slopes
9.4 Properties of OLS under Measurement Error
Measurement Error in the Dependent Variable
Measurement Error in an Explanatory Variable
9.5 Missing Data, Nonrandom Samples, and Outlying Observations
Missing Data
Nonrandom Samples
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
Static Models
Finite Distributed Lag Models
A Convention about the Time Index
10.3 Finite Sample Properties of OLS under Classical Assumptions
Unbiasedness of OLS
The Variances of the OLS Estimators and the Gauss–Markov Theorem
Inference under the Classical Linear Model Assumptions
10.4 Functional Form, Dummy Variables, and Index Numbers
10.5 Trends and Seasonality
Characterizing Trending Time Series
Using Trending Variables in Regression Analysis
A Detrending Interpretation of Regressions with a Time Trend
Computing RSquared When the Dependent Variable Is Trending
Seasonality
Summary
Key Terms
Problems
Computer Exercises
Chapter 11 Future Issues in Using OLS with Time Series Data
11.1 Stationary and Weakly Dependent Time Series
Stationary and Nonstationary Time Series
Weakly Dependent Time Series
11.2 Asymptotic Properties of OLS
11.3 Using Highly Persistent Time Series in Regression Analysis
Highly Persistent Time Series
Transformations on Highly Persistent Time Series
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
Unbiasedness and Consistency
Efficiency and Inference
GoodnessofFit
Serial Correlation in the Presence of Lagged Dependent Variables
12.2 Testing for Serial Correlation
A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors
The Durbin–Watson 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
Feasible GLS Estimation with AR(1) Errors
Comparing OLS and FGLS
Correcting for Higher Order Serial Correlation
12.4 Differencing and Serial Correlation
12.5 Serial CorrelationRobust Inference after OLS
12.6 Heteroskedasticity in Time Series Regressions
HeteroskedasticityRobust Statistics
Testing for Heteroskedasticity
Autoregressive Conditional Heteroskedasticity
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
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
Fixed Effects or First Differencing?
Fixed Effects with Unbalanced Panels
14.2 Random Effects Models
Random Effects or Fixed Effects?
14.3 The Correlated Random Effects Approach
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
Properties of IV with a Poor Instrumental Variable
Computing RSquared after IV Estimation
15.2 IV Estimation of the Multiple Regression Model
15.3 Two Stage Least Squares
A Single Endogenous Explanatory Variable
Multicollinearity and 2SLS
Multiple Endogenous Explanatory Variables
Testing Multiple Hypotheses after 2SLS Estimation
15.4 IV Solutions to ErrorsinVariables Problems
15.5 Testing for Endogeneity and Testing Overidentifying Restrictions
Testing for Endogeneity
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
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
Estimation by 2SLS
16.4 Systems with More Than Two Equations
Identification in Systems with Three or More Equations
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
Specifying Logit and Probit Models
Maximum Likelihood Estimation of Logit and Probit Models
Testing Multiple Hypotheses
Interpreting the Logit and Probit Estimates
17.2 The Tobit Model for Corner Solution Responses
Interpreting the Tobit Estimates
Specification Issues in Tobit Models
17.3 The Poisson Regression Model
17.4 Censored and Truncated Regression Models
Censored Regression Models
Truncated Regression Models
17.5 Sample Selection Corrections
When Is OLS on the Selected Sample Consistent?
Incidental Truncation
Summary
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
Rational Distributed Lag Models
18.2 Testing for Unit Roots
18.3 Spurious Regression
18.4 Cointegration and Error Correction Models
Cointegration
Error Correction Models
18.5 Forecasting
Types of Regression Models Used for Forecasting
OneStepAhead Forecasting
Comparing OneStepAhead Forecasts
MultipleStepAhead Forecasts
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
Deciding on the Appropriate Data Set
Entering and Storing Your Data
Inspecting, Cleaning, and Summarizing Your Data
19.4 Econometric Analysis
19.5 Writing an Empirical Paper
Introduction
Conceptual (or Theoretical) Framework
Econometric Models and Estimation Methods
The Data
Results
Conclusions
Style Hints
Summary
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
The Natural Logarithm
The Exponential Function
A.5 Differential Calculus
Summary
Key Terms
Problems
APPENDIX B Fundamentals of Probability
B.1 Random Variables and Their Probability Distributions
Discrete Random Variables
Continuous Random Variables
B.2 Joint Distributions, Conditional Distributions, and Independence
Joint Distributions and Independence
Conditional Distributions
B.3 Features of Probability Distributions
A Measure of Central Tendency: The Expected Value
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
B.4 Features of Joint and Conditional Distributions
Measures of Association: Covariance and Correlation
Covariance
Correlation Coefficient
Variance of Sums of Random Variables
Conditional Expectation
Properties of Conditional Expectation
Conditional Variance
B.5 The Normal and Related Distributions
The Normal Distribution
The Standard Normal Distribution
Additional Properties of the Normal Distribution
The ChiSquare Distribution
The t Distribution
The F Distribution
Summary
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
Unbiasedness
The Sampling Variance of Estimators
Efficiency
C.3 Asymptotic or Large Sample Properties of Estimators
Consistency
Asymptotic Normality
C.4 General Approaches to Parameter Estimation
Method of Moments
Maximum Likelihood
Least Squares
C.5 Interval Estimation and Confidence Intervals
The Nature of Interval Estimation
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
C.6 Hypothesis Testing
Fundamentals of Hypothesis Testing
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
C.7 Remarks on Notation
Summary
Key Terms
Problems
APPENDIX D Summary of Matrix Algebra
D.1 Basic Definitions
D.2 Matrix Operations
Matrix Addition
Scalar Multiplication
Matrix Multiplication
Transpose
Partitioned Matrix Multiplication
Trace
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
Expected Value
Variance–Covariance Matrix
Multivariate Normal Distribution
ChiSquare Distribution
t Distribution
F Distribution
Summary
Key Terms
Problems
APPENDIX E The Linear Regression Model in Matrix Form
E.1 The Model and Ordinary Least Squares Estimation
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