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Nonparametric Econometrics : Theory and Practice - Qi Li

Nonparametric Econometrics

Theory and Practice

Hardcover

Published: 17th December 2006
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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.

"Overall, the text is a must for graduate students undertaking research in this area; the large number of exercises at the end of each chapter makes it very suitable for a graduate class on nonparametric and semiparametric techniques. In addition, because the coverage of the book is very comprehensive and up-to-date, it constitutes an excellent reference for researchers applying these techniques. Therefore, it can satisfy the needs of both audiences with a solid background in theoretical econometrics and more applied audiences."--Margarita Genius, European Review of Agricultural Economics

Prefacep. xvii
Nonparametric Kernel Methodsp. 1
Density Estimationp. 3
Univariate Density Estimationp. 4
Univariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methodsp. 14
Univariate Bandwidth Selection: Cross-Validation ZMethodsp. 15
Least Squares Cross-Validationp. 15
Likelihood Cross-Validationp. 18
An Illustration of Data-Driven Bandwidth Selectionp. 19
Univariate CDF Estimationp. 19
Univariate CDF Bandwidth Selection: Cross- Validation Methodsp. 23
Multivariate Density Estimationp. 24
Multivariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methodsp. 26
Multivariate Bandwidth Selection: Cross-Validation Methodsp. 27
Least Squares Cross-Validationp. 27
Likelihood Cross-Validationp. 28
Asymptotic Normality of Density Estimatorsp. 28
Uniform Rates of Convergencep. 30
Higher Order Kernel Functionsp. 33
Proof of Theorem 1.4 (Uniform Almost Sure Convergence)p. 35
Applicationsp. 40
Female Wage Inequalityp. 41
Unemployment Rates and City Sizep. 43
Adolescent Growthp. 44
Old Faithful Geyser Datap. 44
Evolution of Real Income Distribution in Italy, 1951-1998p. 45
Exercisesp. 47
Regressionp. 57
Local Constant Kernel Estimationp. 60
Intuition Underlying the Local Constant Kernel Estimatorp. 64
Local Constant Bandwidth Selectionp. 66
Rule-of-Thumb and Plug-In Methodsp. 66
Least Squares Cross-Validationp. 69
AICcp. 72
The Presence of Irrelevant Regressorsp. 73
Some Further Results on Cross-Validationp. 78
Uniform Rates of Convergencep. 78
Local Linear Kernel Estimationp. 79
Local Linear Bandwidth Selection: Least Squares Cross-Validationp. 83
Local Polynomial Regression (General pth Order)p. 85
The Univariate Casep. 85
The Multivariate Casep. 88
Asymptotic Normality of Local Polynomial Estimatorsp. 89
Applicationsp. 92
Prestige Datap. 92
Adolescent Growthp. 92
Inflation Forecasting and Money Growthp. 93
Proofsp. 97
Derivation of (2.24)p. 98
Proof of Theorem 2.7p. 100
Definitions of Al,p+1 and Vl Used in Theorem 2.10p. 106
Exercisesp. 108
Frequency Estimation with Mixed Datap. 115
Probability Function Estimation with Discrete Datap. 116
Regression with Discrete Regressorsp. 118
Estimation with Mixed Data: The Frequency Approachp. 118
Density Estimation with Mixed Datap. 118
Regression with Mixed Datap. 119
Some Cautionary Remarks on Frequency Methodsp. 120
Proofsp. 122
Proof of Theorem 3.1p. 122
Exercisesp. 123
Kernel Estimation with Mixed Datap. 125
Smooth Estimation of Joint Distributions with Discrete Datap. 126
Smooth Regression with Discrete Datap. 131
Kernel Regression with Discrete Regressors: The Irrelevant Regressor Casep. 134
Regression with Mixed Data: Relevant Regressorsp. 136
Smooth Estimation with Mixed Datap. 136
The Cross-Validation Methodp. 138
Regression with Mixed Data: Irrelevant Regressorsp. 140
Ordered Discrete Variablesp. 144
Applicationsp. 145
Food-Away-from-Home Expenditurep. 145
Modeling Strike Volumep. 147
Exercisesp. 150
Conditional Density Estimationp. 155
Conditional Density Estimation: Relevant Variablesp. 155
Conditional Density Bandwidth Selectionp. 157
Least Squares Cross-Validation: Relevant Variablesp. 157
Maximum Likelihood Cross-Validation: Relevant Variablesp. 160
Conditional Density Estimation: Irrelevant Variablesp. 162
The Multivariate Dependent Variables Casep. 164
The General Categorical Data Casep. 167
Proof of Theorem 5.5p. 168
Applicationsp. 171
A Nonparametric Analys
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9780691121611
ISBN-10: 0691121613
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 768
Published: 17th December 2006
Publisher: Princeton University Press
Country of Publication: US
Dimensions (cm): 25.98 x 18.34  x 4.01
Weight (kg): 1.45