Introductory Econometrics: A Modern Approach (5th ed.)

909 Pages·7.56 MB·english

by  Wooldridge J.

Introductory
Econometrics
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Introductory
Econometrics
A ModErn ApproAch
FiFth Edition
Jeffrey M. Wooldridge
Michigan State University
Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States
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Approach, Fifth Edition
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Brief Contents
Chapter 1 The Nature of Econometrics and Economic Data 1
PART 1: Regression Analysis with Cross-Sectional Data 21
Chapter 2 The Simple Regression Model 22
Chapter 3 Multiple Regression Analysis: Estimation 68
Chapter 4 Multiple Regression Analysis: Inference 118
Chapter 5 Multiple Regression Analysis: OLS Asymptotics 168
Chapter 6 Multiple Regression Analysis: Further Issues 186
Chapter 7 Multiple Regression Analysis with Qualitative
Information: Binary (or Dummy) Variables 227
Chapter 8 Heteroskedasticity 268
Chapter 9 More on Specification and Data Issues 303
PART 2: Regression Analysis with Time Series Data 343
Chapter 10 Basic Regression Analysis with Time Series Data 344
Chapter 11 Further Issues in Using OLS with Time Series Data 380
Chapter 12 Serial Correlation and Heteroskedasticity in Time Series Regressions 412
PART 3: Advanced Topics 447
Chapter 13 Pooling Cross Sections Across Time: Simple Panel Data Methods 448
Chapter 14 Advanced Panel Data Methods 484
Chapter 15 Instrumental Variables Estimation and Two Stage Least Squares 512
Chapter 16 Simultaneous Equations Models 554
Chapter 17 Limited Dependent Variable Models and Sample Selection Corrections 583
Chapter 18 Advanced Time Series Topics 632
Chapter 19 Carrying Out an Empirical Project 676
APPenDiCeS
Appendix A Basic Mathematical Tools 703
Appendix B Fundamentals of Probability 722
Appendix C Fundamentals of Mathematical Statistics 755
Appendix D Summary of Matrix Algebra 796
Appendix E The Linear Regression Model in Matrix Form 807
Appendix F Answers to Chapter Questions 821
Appendix G Statistical Tables 831
References 838
Glossary 844
Index 862
v
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Contents
Preface xv 2.3 Properties of oLS on Any Sample of data 35
About the Author xxv Fitted Values and Residuals 35
Algebraic Properties of OLS Statistics 36
Chapter 1 the nature of Goodness-of-Fit 38
econometrics and economic 2.4 Units of Measurement and Functional Form 39
The Effects of Changing Units of Measurement on
Data 1
OLS Statistics 40
Incorporating Nonlinearities in Simple Regression 41
1.1 What is Econometrics? 1
The Meaning of “Linear” Regression 44
1.2 Steps in Empirical Economic Analysis 2
2.5 Expected Values and Variances of the oLS
1.3 the Structure of Economic data 5
Estimators 45
Cross-Sectional Data 5
Unbiasedness of OLS 45
Time Series Data 8
Variances of the OLS Estimators 50
Pooled Cross Sections 9
Estimating the Error Variance 54
Panel or Longitudinal Data 10
2.6 Regression through the origin and Regression
A Comment on Data Structures 11
on a Constant 57
1.4 Causality and the notion of Ceteris Paribus
Summary 58
in Econometric Analysis 12
Key Terms 59
Summary 16
Problems 60
Key Terms 17
Computer Exercises 63
Problems 17
Appendix 2A 66
Computer Exercises 17
Chapter 3 Multiple regression
pArT 1
Analysis: estimation 68
Regression Analysis with
3.1 Motivation for Multiple Regression 69
Cross-Sectional Data 21
The Model with Two Independent Variables 69
The Model with k Independent Variables 71
Chapter 2 the simple regression
3.2 Mechanics and interpretation of ordinary
Model 22 Least Squares 72
Obtaining the OLS Estimates 72
2.1 definition of the Simple Regression Interpreting the OLS Regression Equation 74
Model 22
On the Meaning of “Holding Other Factors
2.2 deriving the ordinary Least Squares Fixed” in Multiple Regression 76
Estimates 27 Changing More Than One Independent Variable
A Note on Terminology 34 Simultaneously 77
vi
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Contents vii
OLS Fitted Values and Residuals 77 4.5 testing Multiple Linear Restrictions:
A “Partialling Out” Interpretation of Multiple the F test 143
Regression 78 Testing Exclusion Restrictions 143
Comparison of Simple and Multiple Regression Relationship between F and t Statistics 149
Estimates 78 The R-Squared Form of the F Statistic 150
Goodness-of-Fit 80 Computing p-Values for F Tests 151
Regression through the Origin 81 The F Statistic for Overall Significance of a
3.3 the Expected Value of the oLS Estimators 83 Regression 152
Including Irrelevant Variables in a Regression Testing General Linear Restrictions 153
Model 88 4.6 Reporting Regression Results 154
Omitted Variable Bias: The Simple Case 88
Summary 157
Omitted Variable Bias: More General Cases 91
Key Terms 159
3.4 the Variance of the oLS Estimators 93
Problems 159
The Components of the OLS Variances:
Computer Exercises 164
Multicollinearity 94
Variances in Misspecified Models 98
Chapter 5 Multiple regression
Estimating s2: Standard Errors of the OLS
Analysis: oLs Asymptotics 168
Estimators 99
3.5 Efficiency of oLS: the Gauss-Markov
5.1 Consistency 169
theorem 101
Deriving the Inconsistency in OLS 172
3.6 Some Comments on the Language of Multiple
5.2 Asymptotic normality and Large Sample
Regression Analysis 103
inference 173
Summary 104
Other Large Sample Tests: The Lagrange
Key Terms 105 Multiplier Statistic 178
Problems 106 5.3 Asymptotic Efficiency of oLS 181
Computer Exercises 110 Summary 182
Appendix 3A 113 Key Terms 183
Problems 183
Chapter 4 Multiple regression Computer Exercises 183
Analysis: inference 118 Appendix 5A 185
4.1 Sampling distributions of the oLS Chapter 6 Multiple regression
Estimators 118 Analysis: further issues 186
4.2 testing hypotheses about a Single Population
Parameter: the t test 121 6.1 Effects of data Scaling on oLS Statistics 186
Testing against One-Sided Alternatives 123 Beta Coefficients 189
Two-Sided Alternatives 128 6.2 More on Functional Form 191
Testing Other Hypotheses about bj 130 More on Using Logarithmic Functional
Computing p-Values for t Tests 133 Forms 191
A Reminder on the Language of Classical Models with Quadratics 194
Hypothesis Testing 135 Models with Interaction Terms 198
Economic, or Practical, versus Statistical 6.3 More on Goodness-of-Fit and Selection
Significance 135 of Regressors 200
4.3 Confidence intervals 138 Adjusted R-Squared 202
4.4 testing hypotheses about a Single Linear Using Adjusted R-Squared to Choose between
Combination of the Parameters 140 Nonnested Models 203
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has
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viii Contents
Controlling for Too Many Factors in Regression Chapter 8 Heteroskedasticity 268
Analysis 205
Adding Regressors to Reduce the Error 8.1 Consequences of heteroskedasticity for
Variance 206 oLS 268
6.4 Prediction and Residual Analysis 207 8.2 heteroskedasticity-Robust inference after oLS
Confidence Intervals for Predictions 207 Estimation 269
Residual Analysis 211 Computing Heteroskedasticity-Robust LM
Predicting y When log(y) Is the Dependent Tests 274
Variable 212 8.3 testing for heteroskedasticity 275
Summary 216 The White Test for Heteroskedasticity 279
Key Terms 217 8.4 Weighted Least Squares Estimation 280
Problems 218 The Heteroskedasticity Is Known up to a
Multiplicative Constant 281
Computer Exercises 220
The Heteroskedasticity Function Must Be
Appendix 6A 225
Estimated: Feasible GLS 286
What If the Assumed Heteroskedasticity Function
Chapter 7 Multiple regression Is Wrong? 290
Analysis with Qualitative Prediction and Prediction Intervals with
Heteroskedasticity 292
information: Binary (or Dummy)
8.5 the Linear Probability Model Revisited 294
Variables 227
Summary 296
7.1 describing Qualitative information 227 Key Terms 297
7.2 A Single dummy independent Problems 297
Variable 228 Computer Exercises 299
Interpreting Coefficients on Dummy
Explanatory Variables When the Dependent
Chapter 9 More on specification
Variable Is log(y) 233
and Data issues 303
7.3 Using dummy Variables for Multiple
Categories 235
Incorporating Ordinal Information by Using 9.1 Functional Form Misspecification 304
Dummy Variables 237 RESET as a General Test for Functional Form
Misspecification 306
7.4 interactions involving dummy Variables 240
Tests against Nonnested Alternatives 307
Interactions among Dummy Variables 240
Allowing for Different Slopes 241 9.2 Using Proxy Variables for Unobserved
Explanatory Variables 308
Testing for Differences in Regression Functions
Using Lagged Dependent Variables as Proxy
across Groups 245
Variables 313
7.5 A Binary dependent Variable: the Linear
A Different Slant on Multiple Regression 314
Probability Model 248
9.3 Models with Random Slopes 315
7.6 More on Policy Analysis and Program
Evaluation 253 9.4 Properties of oLS under Measurement
Error 317
7.7 interpreting Regression Results with discrete
Measurement Error in the Dependent
dependent Variables 256
Variable 318
Summary 257
Measurement Error in an Explanatory
Key Terms 258 Variable 320
Problems 258
9.5 Missing data, nonrandom Samples, and
Computer Exercises 262 outlying observations 324
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has
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