Buch, Englisch, 768 Seiten, Format (B × H): 187 mm x 260 mm, Gewicht: 1471 g
Theory and Practice
Buch, Englisch, 768 Seiten, Format (B × H): 187 mm x 260 mm, Gewicht: 1471 g
ISBN: 978-0-691-12161-1
Verlag: Princeton University Press
Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers.Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data--nominal and ordinal--in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory.This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types--continuous, nominal, and ordinal--within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables.Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.
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Weitere Infos & Material
Preface xvii
PART I: Nonparametric Kernel Methods 1
Chapter 1: Density Estimation 3
1.1 Univariate Density Estimation 4
1.2 Univariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 14
1.3 Univariate Bandwidth Selection: Cross-Validation ZMethods 15
1.3.1 Least Squares Cross-Validation 15
1.3.2 Likelihood Cross-Validation 18
1.3.3 An Illustration of Data-Driven Bandwidth Selection 19
1.4 Univariate CDF Estimation 19
1.5 Univariate CDF Bandwidth Selection: Cross- Validation Methods 23
1.6 Multivariate Density Estimation 24
1.7 Multivariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 26
1.8 Multivariate Bandwidth Selection: Cross-Validation Methods 27
1.8.1 Least Squares Cross-Validation 27
1.8.2 Likelihood Cross-Validation 28
1.9 Asymptotic Normality of Density Estimators 28
1.10 Uniform Rates of Convergence 30
1.11 Higher Order Kernel Functions 33
1.12 Proof of Theorem 1.4 (Uniform Almost Sure Convergence) 35
1.13 Applications 40
1.13.1 Female Wage Inequality 41
1.13.2 Unemployment Rates and City Size 43
1.13.3 Adolescent Growth 44
1.13.4 Old Faithful Geyser Data 44
1.13.5 Evolution of Real Income Distribution in Italy, 1951-1998 45
1.14 Exercises 47
Chapter 2: Regression 57
2.1 Local Constant Kernel Estimation 60
2.1.1 Intuition Underlying the Local Constant Kernel Estimator 64
2.2 Local Constant Bandwidth Selection 66
2.2.1 Rule-of-Thumb and Plug-In Methods 66
2.2.2 Least Squares Cross-Validation 69
2.2.3 AICc 72
2.2.4 The Presence of Irrelevant Regressors 73
2.2.5 Some Further Results on Cross-Validation 78
2.3 Uniform Rates of Convergence 78
2.4 Local Linear Kernel Estimation 79
2.4.1 Local Linear Bandwidth Selection: Least Squares Cross-Validation 83
2.5 Local Polynomial Regression (General pth Order) 85
2.5.1 The Univariate Case 85
2.5.2 The Multivariate Case 88
2.5.3 Asymptotic Normality of Local Polynomial Estimators 89
2.6 Applications 92
2.6.1 Prestige Data 92
2.6.2 Adolescent Growth 92
2.6.3 Inflation Forecasting and Money Growth 93
2.7 Proofs 97
2.7.1 Derivation of (2.24) 98
2.7.2 Proof of Theorem 2.7 100
2.7.3 Definitions of Al,p+1 and Vl Used in Theorem 2.10 106
2.8 Exercises 108
Chapter 3: Frequency Estimation with Mixed Data 115
3.1 Probability Function Estimation with Discrete Data 116
3.2 Regression with Discrete Regressors 118
3.3 Estimation with Mixed Data: The Frequency Approach 118
3.3.1 Density Estimation with Mixed Data 118
3.3.2 Regression with Mixed Data 119
3.4 Some Cautionary Remarks on Frequency Methods 120
3.5 Proofs 122
3.5.1 Proof of Theorem 3.1 122
3.6 Exercises 123
Chapter 4: Kernel Estimation with Mixed Data 125
4.1 Smooth Estimation of Joint Distributions with Discrete Data 126
4.2 Smooth Regression with Discrete Data 131
4.3 Kernel Regression with Discrete Regressors: The Irrelevant Regressor Case 134
4.4 Regression with Mixed Data: Relevant Regressors 136
4.4.1 Smooth Estimation with Mixed Data 136
4.4.2 The Cross-Validation Method 138
4.5 Regression with Mixed Data: Irrelevant Regressors 140
4.5.1 Ordered Discrete Variables 144
4.6 Applications 145
4.6.1 Food-Away-from-Home Expenditure 145
4.6.2 Modeling Strike Volume 147
4.7 Exercises 150
Chapter 5: Conditional Density Estimation 155
5.1 Conditional Density Estimation: Relevant Variables 155
5.2 Conditional Density Bandwidth Selection 157
5.2.1 Least Squares Cross-Validation: Relevant Variables 157
5.2.2 Maximum Likelihood Cross-Validation: Relevant Variables 160
5.3 Conditional Density Estimation: Irrelevant Variables 162
5.4 The Multivariate Dependent Variables Case 164
5.4.1 The General Categorical Data Case 167
5.4.2 Proof of Theorem 5.5 168
5.5 Applications 171
5.5.1 A Nonparametric Analysis of Corruption 171
5.5.2 Extramarital A
