Nonparametric Econometrics Theory and Practice

ISBN-10: 0691121613

ISBN-13: 9780691121611

Edition: 2007

List price: $130.00
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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.
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Book details

List price: $130.00
Copyright year: 2007
Publisher: Princeton University Press
Publication date: 12/17/2006
Binding: Hardcover
Pages: 768
Size: 7.25" wide x 10.25" long x 1.50" tall
Weight: 3.476
Language: English

Nonparametric Kernel Methods
Density Estimation
Univariate Density Estimation
Univariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods
Univariate Bandwidth Selection: Cross-Validation ZMethods
Least Squares Cross-Validation
Likelihood Cross-Validation
An Illustration of Data-Driven Bandwidth Selection
Univariate CDF Estimation
Univariate CDF Bandwidth Selection: Cross- Validation Methods
Multivariate Density Estimation
Multivariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods
Multivariate Bandwidth Selection: Cross-Validation Methods
Least Squares Cross-Validation
Likelihood Cross-Validation
Asymptotic Normality of Density Estimators
Uniform Rates of Convergence
Higher Order Kernel Functions
Proof of Theorem 1.4 (Uniform Almost Sure Convergence)
Female Wage Inequality
Unemployment Rates and City Size
Adolescent Growth
Old Faithful Geyser Data
Evolution of Real Income Distribution in Italy, 1951-1998
Local Constant Kernel Estimation
Intuition Underlying the Local Constant Kernel Estimator
Local Constant Bandwidth Selection
Rule-of-Thumb and Plug-In Methods
Least Squares Cross-Validation
The Presence of Irrelevant Regressors
Some Further Results on Cross-Validation
Uniform Rates of Convergence
Local Linear Kernel Estimation
Local Linear Bandwidth Selection: Least Squares Cross-Validation
Local Polynomial Regression (General pth Order)
The Univariate Case
The Multivariate Case
Asymptotic Normality of Local Polynomial Estimators
Prestige Data
Adolescent Growth
Inflation Forecasting and Money Growth
Derivation of (2.24)
Proof of Theorem 2.7
Definitions of Al,p+1 and Vl Used in Theorem 2.10
Frequency Estimation with Mixed Data
Probability Function Estimation with Discrete Data
Regression with Discrete Regressors
Estimation with Mixed Data: The Frequency Approach
Density Estimation with Mixed Data
Regression with Mixed Data
Some Cautionary Remarks on Frequency Methods
Proof of Theorem 3.1
Kernel Estimation with Mixed Data
Smooth Estimation of Joint Distributions with Discrete Data
Smooth Regression with Discrete Data
Kernel Regression with Discrete Regressors: The Irrelevant Regressor Case
Regression with Mixed Data: Relevant Regressors
Smooth Estimation with Mixed Data
The Cross-Validation Method
Regression with Mixed Data: Irrelevant Regressors
Ordered Discrete Variables
Food-Away-from-Home Expenditure
Modeling Strike Volume
Conditional Density Estimation
Conditional Density Estimation: Relevant Variables
Conditional Density Bandwidth Selection
Least Squares Cross-Validation: Relevant Variables
Maximum Likelihood Cross-Validation: Relevant Variables
Conditional Density Estimation: Irrelevant Variables
The Multivariate Dependent Variables Case
The General Categorical Data Case
Proof of Theorem 5.5
A Nonparametric Analys
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