Introductory Econometrics: A Modern Approach, Seventh Edition 

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Comment from the Stata technical groupThe seventh 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 seventh edition introduces discrete covariates and the modern potential outcome approach to causal inference earlier and more accessibly in the new sections 2.7, 3.7, and 4.7 and in the improved section 7.6. The new subsections 13.2(a) and 13.2(b) clearly explain the differenceindifferences approach to causal inference. 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
11 What Is Econometrics?
12 Steps in Empirical Economic Analysis 13 The Structure of Economic Data
13a CrossSectional Data
14 Causality, Ceteris Paribus, and Counterfactual Reasoning 13b Time Series Data 13c Pooled Cross Sections 13d Panel or Longitudinal Data 13e A Comment on Data Structures Summary Key Terms Problems Computer Exercises PART 1 Regression Analysis with CrossSectional Data
Chapter 2 The Simple Regression Model
21 Definition of the Simple Regression Model
22 Deriving the Ordinary Least Squares Estimates
22a A Note on Terminology
23 Properties of OLS on Any Sample of Data
23a Fitted Values and Residuals
24 Units of Measurement and Functional Form
23b Algebraic Properties of OLS Statistics 24c GoodnessofFit
24a The Effects of Changing Units of Measurement on OLS Statistics
25 Expected Values and Variances of the OLS Estimators
24b Incorporating Nonlinearities in Simple Regression 24c The Meaning of "Linear" Regression
25a Unbiasedness of OLS
26 Regression through the Origin and Regression on a Constant 25b Variances of the OLS Estimators 25c Estimating the Error Variance 27 Regression on a Binary Explanatory Variable
27a Counterfactual Outcomes, Causality, and Policy Analysis
Summary Key Terms Problems Computer Exercises Chapter 3 Multiple Regression Analysis: Estimation
31 Motivation for Multiple Regression
31a The Model with Two Independent Variables
32 Mechanics and Interpretation of Ordinary Least Squares
31b The Model with k Independent Variables
32a Obtaining the OLS Estimates
33 The Expected Value of the OLS Estimators
32b Interpreting the OLS Regression Equation 32c On the Meaning of "Holding Other Factors Fixed" in Multiple Regression 32d Changing More Than One Independent Variable Simultaneously 32e OLS Fitted Values and Residuals 32f A "Partialling Out" Interpretation of Multiple Regression 32g Comparison of Simple and Multiple Regression Estimates 32h GoodnessofFit 32i Regression through the Origin
33a Including Irrelevant Variables in a Regression Model
34 The Variance of the OLS Estimators
33b Omitted Variable Bias: The Simple Case 33c Omitted Variable Bias: More General Cases
34a The Components of the OLS Variances: Multicollinearity
35 Efficiency of OLS: The GaussMarkov Theorem 34b Variances in Misspecified Models 34c Estimating σ^{2}: Standard Errors of the OLS Estimators 36 Some Comments on the Language of Multiple Regression Analysis 37 Several Scenarios for Applying Multiple Regression
37a Prediction
37b Efficient Markets 37c Measuring the Tradeoff between Two Variables 37d Testing for Ceteris Paribus Group Differences 37e Potential Outcomes, Treatment Effects, and Policy Analysis Summary Key Terms Problems Computer Exercises Chapter 4 Multiple Regression Analysis: Inference
41 Sampling Distributions of the OLS Estimators
42 Testing Hypotheses about a Single Population Parameter: The t Test
42a Testing against OneSided Alternatives
43 Confidence Intervals 42b TwoSided Alternatives 42c Testing Other Hypotheses about β_{j} 42d Computing pValues for t Tests 42e A Reminder on the Language of Classical Hypothesis Testing 42f Economic, or Practical, versus Statistical Significance 44 Testing Hypotheses about a Single Linear Combination of the Parameters 45 Testing Multiple Linear Restrictions: The F Test
45a Testing Exclusion Restrictions
46 Reporting Regression Results 45b Relationship between F and t Statistics 45c The RSquared Form of the F Statistic 45d Computing pValues for F Tests 45e The F Statistic for Overall Significance of a Regression 45f Testing General Linear Restrictions 47 Revisiting Causal Effects and Policy Analysis Summary Key Terms Problems Computer Exercises Chapter 5 Multiple Regression Analysis: OLS Asymptotics
51 Consistency
51a Deriving the Inconsistency in OLS
52 Asymptotic Normality and Large Sample Inference
52a Other Large Sample Tests: The Lagrange Multiplier Statistic
53 Asymptotic Efficiency of OLS Summary Key Terms Problems Computer Exercises Chapter 6 Multiple Regression Analysis: Further Issues
61 Effects of Data Scaling on OLS Statistics
61a Beta Coefficients
62 More on Functional Form
62a More on Using Logarithmic Functional Forms
63 More on GoodnessofFit and Selection of Regressors
62b Models with Quadratics 62c Models with Interaction Terms 63d Computing Average Partial Effects
63a Adjusted RSquared
64 Prediction and Residual Analysis
63b Using Adjusted RSquared to Choose between Nonnested Models 63c Controlling for Too Many Factors in Regression Analysis 63d Adding Regressors to Reduce the Error Variance
64a Confidence Intervals for Predictions
Summary 64b Residual Analysis 64c Predicting y When log(y) Is the Dependent Variable 64d Predicting y When the Dependent Variable Is log(y): Key Terms Problems Computer Exercises Chapter 7 Multiple Regression Analysis with Qualitative Information
71 Describing Qualitative Information
72 A Single Dummy Independent Variable
72a Interpreting Coefficients on Dummy Explanatory Variables When the Dependent Variable Is log(y)
73 Using Dummy Variables for Multiple Categories
73a Incorporating Ordinal Information by Using Dummy Variables
74 Interactions Involving Dummy Variables
74a Interactions among Dummy Variables
75 A Binary Dependent Variable: The Linear Probability Model 74b Allowing for Different Slopes 74c Testing for Differences in Regression Functions across Groups 76 More on Policy Analysis and Program Evaluation
76a Program Evaluation and Unrestricted Regression Adjustment
77 Interpreting Regression Results with Discrete Dependent Variables Summary Key Terms Problems Computer Exercises Chapter 8 Heteroskedasticity
81 Consequences of Heteroskedasticity for OLS
82 HeteroskedasticityRobust Inference after OLS Estimation
82a Computing HeteroskedasticityRobust LM Tests
83 Testing for Heteroskedasticity
83a The White Test for Heteroskedasticity
84 Weighted Least Squares Estimation
84a The Heteroskedasticity Is Known up to a Multiplicative Constant
85 The Linear Probability Model Revisited 84b The Heteroskedasticity Function Must Be Estimated: Feasible GLS 84c What If the Assumed Heteroskedasticity Function Is Wrong? 84d Prediction and Prediction Intervals with Heteroskedasticity Summary Key Terms Problems Computer Exercises Chapter 9 More on Specification and Data Issues
91 Functional Form Misspecification
91a RESET as a General Test for Functional Form Misspecification
92 Using Proxy Variables for Unobserved Explanatory Variables
91b Tests against Nonnested Alternatives
92a Using Lagged Dependent Variables as Proxy Variables
93 Models with Random Slopes 92b A Different Slant on Multiple Regression 92c Potential Outcomes and Proxy Variables 94 Properties of OLS under Measurement Error
94a Measurement Error in the Dependent Variable
95 Missing Data, Nonrandom Samples, and Outlying Observations
94b Measurement Error in an Explanatory Variable
95a Missing Data
96 Least Absolute Deviations Estimation 95b Nonrandom Samples 95c 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
101 The Nature of Time Series Data
102 Examples of Time Series Regression Models
102a Static Models
103 Finite Sample Properties of OLS under Classical Assumptions
102b Finite Distributed Lag Models 102c A Convention about the Time Index
103a Unbiasedness of OLS
104 Functional Form, Dummy Variables, and Index Numbers 103b The Variances of the OLS Estimators and the GaussMarkov Theorem 103c Inference under the Classical Linear Model Assumptions 105 Trends and Seasonality
105a Characterizing Trending Time Series
Summary 105b Using Trending Variables in Regression Analysis 105c A Detrending Interpretation of Regressions with a Time Trend 105d Computing RSquared When the Dependent Variable Is Trending 105e Seasonality Key Terms Problems Computer Exercises Chapter 11 Further Issues in Using OLS with Time Series Data
111 Stationary and Weakly Dependent Time Series
111a Stationary and Nonstationary Time Series
112 Asymptotic Properties of OLS 111b Weakly Dependent Time Series 113 Using Highly Persistent Time Series in Regression Analysis
113a Highly Persistent Time Series
114 Dynamically Complete Models and the Absence of Serial Correlation 113b Transformations on Highly Persistent Time Series 113c Deciding Whether a Time Series Is I(1) 115 The Homoskedasticity Assumption for Time Series Models Summary Key Terms Problems Computer Exercises Chapter 12 Serial Correlation and Heteroskedasticity in Time Series Regressions
121 Properties of OLS with Serially Correlated Errors
121a Unbiasedness and Consistency
122 Serial CorrelationRobust Inference after OLS 121b Efficiency and Inference 121c GoodnessofFit 121d Serial Correlation in the Presence of Lagged Dependent Variables 123 Testing for Serial Correlation 123a A t Test for AR(1) Serial Correlation with Strictly Exogenous Regressors 123b The DurbinWatson Test under Classical Assumptions 123c Testing for AR(1) Serial Correlation without Strictly Exogenous Regressors 123d Testing for HigherOrder Serial Correlation 124 Correcting for Serial Correlation with Strictly Exogenous Regressors
124a Obtaining the Best Linear Unbiased Estimator in the AR(1) Model
125 Differencing and Serial Correlation 124b Feasible GLS Estimation with AR(1) Errors 124c Comparing OLS and FGLS 124d Correcting for HigherOrder Serial Correlation 124e What if the Serial Correlation Models Is Wrong? 126 Heteroskedasticity in Time Series Regressions
126a HeteroskedasticityRobust Statistics
Summary 126b Testing for Heteroskedasticity 126c Autoregressive Conditional Heteroskedasticity 126d 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
131 Pooling Independent Cross Sections across Time
131a The Chow Test for Structural Change across Time
132 Policy Analysis with Pooled Cross Sections
132a Adding an Additional Control Group
133 TwoPeriod Panel Data Analysis
132b A General Framework for Policy Analysis with Pooled Cross Sections
133a Organizing Panel Data
134 Policy Analysis with TwoPeriod Panel Data 135 Differencing with More Than Two Time Periods
135a Potential Pitfalls in First Differencing Panel Data
Summary Key Terms Problems Computer Exercises Chapter 14 Advanced Panel Data Methods
141 Fixed Effects Estimation
141a The Dummy Variable Regression
142 Random Effects Models
141b Fixed Effects or First Differencing? 141c Fixed Effects with Unbalanced Panels
142a Random Effects of Pooled OLS?
143 The Correlated Random Effects Approach
142b Random Effects or Fixed Effects?
143a Unbalanced Panels
144 General Policy Analysis with Panel Data
144a Advanced Considerations with Policy Analysis
145 Applying Panel Data Methods to Other Data Structures Summary Key Terms Problems Computer Exercises Chapter 15 Instrumental Variables Estimation and TwoStage Least Squares
151 Motivation: Omitted Variables in a Simple Regression Model
151a Statistical Inference with the IV Estimator
152 IV Estimation of the Multiple Regression Model 151b Properties of IV with a Poor Instrumental Variable 151c Computing RSquared after IV Estimation 153 TwoStage Least Squares
153a A Single Endogenous Explanatory Variable
154 IV Solutions to ErrorsinVariables Problems 153b Multicollinearity and 2SLS 153c Detecting Weak Instruments 153d Multiple Endogenous Explanatory Variables 153e Testing Multiple Hypotheses after 2SLS Estimation 155 Testing for Endogeneity and Testing Overidentifying Restrictions
155a Testing for Endogeneity
156 2SLS with Heteroskedasticity 155b Testing Overidentification Restrictions 157 Applying 2SLS to Time Series Equations 158 Applying 2SLS to Pooled Cross Sections and Panel Data Summary Key Terms Problems Computer Exercises Chapter 16 Simultaneous Equations Models
161 The Nature of Simultaneous Equations Models
162 Simultaneity Bias in OLS 163 Identifying and Estimating a Structural Equation
163a Identification in a TwoEquation System
164 Systems with More Than Two Equations
163b Estimation by 2SLS
164a Identification in Systems with Three or More Equations
165 Simultaneous Equations Models with Time Series 164b Estimation 166 Simultaneous Equations Models with Panel Data Summary Key Terms Problems Computer Exercises Chapter 17 Limited Dependent Variable Models and Sample Selection Corrections
171 Logit and Probit Models for Binary Response
171a Specifying Logit and Probit Models
172 The Tobit Model for Corner Solution Responses
171b Maximum Likelihood Estimation of Logit and Probit Models 171c Testing Multiple Hypotheses 171d Interpreting the Logit and Probit Estimates
172a Interpreting the Tobit Estimates
173 The Poisson Regression Model 172b Specification Issues in Tobit Models 174 Censored and Truncated Regression Models
174a Censored Regression Models
175 Sample Selection Corrections
174b Truncated Regression Models
175a When Is OLS on the Selected Sample Consistent?
Summary 175b Incidental Truncation Key Terms Problems Computer Exercises Chapter 18 Advanced Time Series Topics
181 Infinite Distributed Lag Models
181a The Geometric (or Koyck) Distributed Lag Model
182 Testing for Unit Roots 181b Rational Distributed Lag Models 183 Spurious Regression 184 Cointegration and Error Correction Models
184a Cointegration
185 Forecasting
184b Error Correction Models
185a Types of Regression Models Used for Forecasting
Summary 185b OneStepAhead Forecasting 185c Comparing OneStepAhead Forecasts 185d MultipleStepAhead Forecasts 185e Forecasting Trending, Seasonal, and Integrated Processes Key Terms Problems Computer Exercises Chapter 19 Carrying Out an Empirical Project
191 Posing a Question
192 Literature Review 193 Data Collection
193a Deciding on the Appropriate Data Set
194 Econometric Analysis 193b Entering and Storing Your Data 193c Inspecting, Cleaning, and Summarizing Your Data 195 Writing an Empirical Paper
195a Introduction
Summary 195b Conceptual (or Theoretical) Framework 195c Econometric Models and Estimation Methods 195d The Data 195e Results 195f Conclusions 195g Style Hints Key Terms Sample Empirical Projects List of Journals Data Sources MATH REFRESHER A Basic Mathematical Tools
A1 The Summation Operator and Descriptive Statistics
A2 Properties of Linear Functions A3 Proportions and Percentages A4 Some Special Functions and Their Properties
A4a Quadratic Functions
A5 Differential Calculus A4b The Natural Logarithm A4c The Exponential Function Summary Key Terms Problems MATH REFRESHER B Fundamentals of Probability
B1 Random Variables and Their Probability Distributions
B1a Discrete Random Variables
B2 Joint Distributions, Conditional Distributions, and Independence
B1b Continuous Random Variables
B2a Joint Distributions and Independence
B3 Features of Probability Distributions
B2b Conditional Distributions
B3a A Measure of Central Tendency: The Expected Value
B4 Features of Joint and Conditional Distributions
B3b Properties of Expected Values B3c Another Measure of Central Tendency: The Median B3d Measures of Variability: Variance and Standard Deviation B3e Variance B3f Standard Deviation B3g Standardizing a Random Variable B3h Skewness and Kurtosis
B4a Measures of Association: Covariance and Correlation
B5 The Normal and Related Distributions
B4b Covariance B4c Correlation Coefficient B4d Variance of Sums of Random Variables B4e Conditional Expectation B4f Properties of Conditional Expectation B4g Conditional Variance
B5a The Normal Distribution
Summary B5b The Standard Normal Distribution B5c Additional Properties of the Normal Distribution B5d The ChiSquare Distribution B5e The t Distribution B5f The F Distribution Key Terms Problems MATH REFRESHER C Fundamentals of Mathematical Statistics
C1 Populations, Parameters, and Random Sampling
C1a Sampling
C2 Finite Sample Properties of Estimators
C2a Estimators and Estimates
C3 Asymptotic or Large Sample Properties of Estimators
C2b Unbiasedness C2c The Sampling Variance of Estimators C2d Efficiency
C3a Consistency
C4 General Approaches to Parameter Estimation
C3b Asymptotic Normality
C4a Method of Moments
C5 Interval Estimation and Confidence Intervals
C4b Maximum Likelihood C4c Least Squares
C5a The Nature of Interval Estimation
C6 Hypothesis Testing
C5b Confidence Intervals for the Mean from a Normally Distributed Population C5c A Simple Rule of Thumb for a 95% Confidence Interval C5d Asymptotic Confidence Intervals for Nonnormal Populations
C6a Fundamentals of Hypothesis Testing
C7 Remarks on Notation C6b Testing Hypotheses about the Mean in a Normal Population C6c Asymptotic Tests for Nonnormal Populations C6d Computing and Using pValues C6e The Relationship between Confidence Intervals and Hypothesis Testing C6f Practical versus Statistical Significance Summary Key Terms Problems ADVANCED TREATMENT D Summary of Matrix Algebra
D1 Basic Definitions
D2 Matrix Operations
D2a Matrix Addition
D3 Linear Independence and Rank of a Matrix D2b Scalar Multiplication D2c Matrix Multiplication D2d Transpose D2e Partitioned Matrix Multiplication D2f Trace D2g Inverse D4 Quadratic Forms and Positive Definite Matrices D5 Idempotent Matrices D6 Differentiation of Linear and Quadratic Forms D7 Moments and Distributions of Random Vectors
D7a Expected Value
Summary D7b VarianceCovariance Matrix D7c Multivariate Normal Distribution D7d ChiSquare Distribution D7e t Distribution D7f F Distribution Key Terms Problems ADVANCED TREATMENT E The Linear Regression Model in Matrix Form
E1 The Model and Ordinary Least Squares Estimation
E1a The FrischWaugh Theorem
E2 Finite Sample Properties of OLS E3 Statistical Inference E4 Some Asymptotic Analysis
E4a Wald Statistics for Testing Multiple Hypotheses
Summary Key Terms Problems Answers to Going Further Questions Statistical Tables References Glossary Index 
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