Analysis of Microdata (eBook)

Preface 5
Contents 7
1 Introduction 11
1.1 What Are Microdata? 11
1.2 Types of Microdata 14
1.3 Why Not Linear Regression? 18
1.4 Common Elements of Microdata Models 20
1.5 Examples 21
1.6 Overview of the Book 29
2 From Regression to Probability Models 31
2.1 Introduction 31
2.2 Conditional Probability Functions 33
2.3 Probability and Probability Distributions 39
2.4 Further Exercises 49
3 Maximum Likelihood Estimation 55
3.1 Introduction 55
3.2 Likelihood Function 56
3.3 Properties of the Maximum Likelihood Estimator 63
3.4 Normal Linear Model 73
3.5 Further Aspects of Maximum Likelihood Estimation 77
3.6 Testing 86
3.7 Pros and Cons of Maximum Likelihood 99
3.8 Further Exercises 100
4 Binary Response Models 105
4.1 Introduction 105
4.2 Models for Binary Response Variables 107
4.3 Discrete Choice Models 117
4.4 Estimation 120
4.5 Goodness-of-Fit 132
4.6 Non-Standard Sampling Schemes 137
4.7 Further Exercises 140
5 Multinomial Response Models 147
5.1 Introduction 147
5.2 Multinomial Logit Model 149
5.3 Conditional Logit Model 160
5.4 Generalized Multinomial Response Models 170
5.5 Further Exercises 176
6 Ordered Response Models 181
6.1 Introduction 181
6.2 Standard Ordered Response Models 184
6.3 Generalized Threshold Models 198
6.4 Sequential Models 204
6.5 Interval Data 210
6.6 Further Exercises 212
7 Limited Dependent Variables 217
7.1 Introduction 217
7.2 Tobin’s Corner Solution Model 221
7.3 Sample Selection Models 234
7.4 Treatment Effect Models 249
7.5 Appendix: Bivariate Normal Distribution 256
7.6 Further Exercises 257
8 Event History Models 261
8.1 Introduction 261
8.2 Duration Models 264
8.3 Count Data Models 289
8.4 Further Exercises 304
List of Figures 307
List of Tables 309
References 311
Index 319