Using Econometrics A Practical Guide-7th-Edition by Studenmund

CONTENTS
Preface xiii
Chapter 1 An Overview of Regression Analysis 1
1.1 What Is Econometrics? 1
1.2 What Is Regression Analysis? 5
1.3 The Estimated Regression Equation 14
1.4 A Simple Example of Regression Analysis 17
1.5 Using Regression Analysis to Explain Housing Prices 20
1.6 Summary and Exercises 23
1.7 Appendix: Using Stata 30
Chapter 2 Ordinary Least Squares 35
2.1 Estimating Single-Independent-Variable
Models with OLS 35
2.2 Estimating Multivariate Regression Models with OLS 40
2.3 Evaluating the Quality of a Regression Equation 49
2.4 Describing the Overall Fit of the Estimated Model 50
2.5 An Example of the Misuse of R
2 55
2.6 Summary and Exercises 57
2.7 Appendix: Econometric Lab #1 63
Chapter 3 Learning to Use Regression Analysis 65
3.1 Steps in Applied Regression Analysis 66
3.2 Using Regression Analysis to Pick Restaurant Locations 73
3.3 Dummy Variables 79
3.4 Summary and Exercises 83
3.5 Appendix: Econometric Lab #2 89
Chapter 4 The Classical Model 92
4.1 The Classical Assumptions 92
4.2 The Sampling Distribution of n
100
4.3 The Gauss–Markov Theorem and the Properties
of OLS Estimators 106
4.4 Standard Econometric Notation 107
4.5 Summary and Exercises 108
Chapter 5 Hypothesis Testing and Statistical Inference 115
5.1 What Is Hypothesis Testing? 116
5.2 The t-Test 121
5.3 Examples of t-Tests 129
5.4 Limitations of the t-Test 137
5.5 Confidence Intervals 139
5.6 The F-Test 142
5.7 Summary and Exercises 147
5.8 Appendix: Econometric Lab #3 155
Chapter 6 Specification: Choosing the Independent
Variables
157
6.1 Omitted Variables 158
6.2 Irrelevant Variables 165
6.3 An Illustration of the Misuse of Specification Criteria 167
6.4 Specification Searches 169
6.5 An Example of Choosing Independent Variables 174
6.6 Summary and Exercises 177
6.7 Appendix: Additional Specification Criteria 184
Chapter 7 Specification: Choosing a Functional Form 189
7.1 The Use and Interpretation of the Constant Term 190
7.2 Alternative Functional Forms 192
7.3 Lagged Independent Variables 202
7.4 Slope Dummy Variables 203
7.5 Problems with Incorrect Functional Forms 206
7.6 Summary and Exercises 209
7.7 Appendix: Econometric Lab #4 217
Chapter 8 Multicollinearity 221
8.1 Perfect versus Imperfect Multicollinearity 222
8.2 The Consequences of Multicollinearity 226
8.3 The Detection of Multicollinearity 232
8.4 Remedies for Multicollinearity 235
8.5 An Example of Why Multicollinearity Often Is Best Left
Unadjusted 238
8.6 Summary and Exercises 240
8.7 Appendix: The SAT Interactive Regression
Learning Exercise 244
Chapter 9 Serial Correlation 273
9.1 Time Series 274
9.2 Pure versus Impure Serial Correlation 275
9.3 The Consequences of Serial Correlation 281
9.4 The Detection of Serial Correlation 284
9.5 Remedies for Serial Correlation 291
9.6 Summary and Exercises 296
9.7 Appendix: Econometric Lab #5 303
Chapter 10 Heteroskedasticity 306
10.1 Pure versus Impure Heteroskedasticity 307
10.2 The Consequences of Heteroskedasticity 312
10.3 Testing for Heteroskedasticity 314
10.4 Remedies for Heteroskedasticity 320
10.5 A More Complete Example 324
10.6 Summary and Exercises 330
10.7 Appendix: Econometric Lab #6 337
Chapter 11 Running Your Own Regression Project 340
11.1 Choosing Your Topic 341
11.2 Collecting Your Data 342
11.3 Advanced Data Sources 346
11.4 Practical Advice for Your Project 348
11.5 Writing Your Research Report 352
11.6 A Regression User’s Checklist and Guide 353
11.7 Summary 357
11.8 Appendix: The Housing Price Interactive Exercise 358
Chapter 12 Time-Series Models 364
12.1 Distributed Lag Models 365
12.2 Dynamic Models 367
12.3 Serial Correlation and Dynamic Models 371
12.4 Granger Causality 374
12.5 Spurious Correlation and Nonstationarity 376
12.6 Summary and Exercises 385
Chapter 13 Dummy Dependent Variable Techniques 390
13.1 The Linear Probability Model 390
13.2 The Binomial Logit Model 397
13.3 Other Dummy Dependent Variable Techniques 404
13.4 Summary and Exercises 406
Chapter 14 Simultaneous Equations 411
14.1 Structural and Reduced-Form Equations 412
14.2 The Bias of Ordinary Least Squares 418
14.3 Two-Stage Least Squares (2SLS) 421
14.4 The Identification Problem 430
14.5 Summary and Exercises 435
14.6 Appendix: Errors in the Variables 440
Chapter 15 Forecasting 443
15.1 What Is Forecasting? 444
15.2 More Complex Forecasting Problems 449
15.3 ARIMA Models 456
15.4 Summary and Exercises 459
Chapter 16 Experimental and Panel Data 465
16.1 Experimental Methods in Economics 466
16.2 Panel Data 473
16.3 Fixed versus Random Effects 483
16.4 Summary and Exercises 484
Appendix A Answers 491
Appendix B Statistical Tables 517
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