Nonparametric Econometrics

ISBN-10: 0521586119
ISBN-13: 9780521586115
Edition: 1999
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Description: Covering the vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the last five decades, this book will be useful for first year graduate courses in econometrics.

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Book details

List price: $57.00
Copyright year: 1999
Publisher: Cambridge University Press
Publication date: 6/13/1999
Binding: Paperback
Pages: 444
Size: 6.25" wide x 9.25" long x 1.00" tall
Weight: 1.298
Language: English

Covering the vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the last five decades, this book will be useful for first year graduate courses in econometrics.

Michael Wickens is professor of economics at the University Michael Wickens is professor of economics at the University of York. He is the coeditor of "Handbook of Applied Econometof York. He is the coeditor of "Handbook of Applied Econometrics" and was managing editor of the" Economic Journal" fromrics" and was managing editor of the" Economic Journal" from 1996 to 2004. 1996 to 2004.

Preface
Introduction
Methods of Density Estimation
Introduction
Nonparametric Density Estimation
A "Local" Histogram Approach
A Formal Derivation of andfirac;[subscript 1] (x)
Rosenblatt-Parzen Kernel Estimator
The Nearest Neighborhood Estimator
Variable Window-Width Estimators
Series Estimators
Penalized Likelihood Estimators
The Local Log-Likelihood Estimators
Summary
Estimation of Derivatives of a Density
Finite-Sample Properties of the Kernel Estimator
The Exact Bias and Variance of the Estimator andfirac;
Approximations to the Bias and Variance and Choices of h and K
Reduction of Bias
Asymptotic Properties of the Kernel Density Estimator andfirac; with Independent Observations
Asymptotic Unbiasedness
Consistency
Asymptotic Normality
Small-Sample Confidence Intervals
Sampling Properties of the Kernel Density Estimator with Dependent Observations
Unbiasedness
Consistency
Asymptotic Normality
Bibliographical Summary (Approximate and Asymptotic Results)
Choices of Window Width and Kernel: Further Discussion
Choice of h
Choice of Higher Order Kernels
Choice of h for Density Derivatives
Multivariate Density Estimation
Testing Hypotheses about Densities
Comparison with a Known Density Function
Testing for Symmetry
Comparison of Unknown Densities
Testing for Independence
Examples
Density of Stock Market Returns
Estimating the Dickey-Fuller Density
Conditional Moment Estimation
Introduction
Estimating Conditional Moments by Kernel Methods
Parametric Estimation
Nonparametric Estimation: A "Local" Regression Approach
Kernel-Based Estimation: A Formal Derivation
A General Nonparametric Estimator of m(x)
Unifying Nonparametric Estimators
Estimation of Higher Order Conditional Moments
Finite-Sample Properties
Approximate Results: Stochastic x
The Local Linear Regression Estimator
Combining Parametric and Nonparametric Estimators
Asymptotic Properties
Asymptotic Properties of the Kernel Estimator with Independent Observations
Asymptotic Properties of the Kernel Estimator with Dependent Observations
Bibliographical Summary (Asymptotic Results)
Implementing the Kernel Estimator
Choice of Window Width
Robust Nonparametric Estimation of Moments
Estimating Conditional Moments by Series Methods
Asymptotic Properties of Series Estimators with Independent Observations
Asymptotic Properties of Series Estimators with Dependent Observations
Implementing the Estimator
Imposing Structure on the Conditional Moments
Generalized Additive Models
Projection Pursuit Regression
Neural Networks
Measuring the Affinity of Parametric and Nonparametric Models
Examples
A Model of Strike Duration
Earnings-Age Profiles
Review of Applied Work on Nonparametric Regression
Nonparametric Estimation of Derivatives
Introduction
The Model and Partial Derivative Formulae
Estimation
Estimation of Partial Derivatives by Kernel Methods
Estimation of Partial Derivatives by Series Methods
Estimation of Average Derivatives
Local Linear Derivative Estimators
Pointwise Versus Average Derivatives
Restricted Estimation and Hypothesis Testing
Imposing Linear Equality Restriction on Partial Derivatives
Imposing Linear Inequality Restrictions
Hypothesis Testing
Asymptotic Properties of Partial Derivative Estimators
Asymptotic Properties of Kernel-Based Estimators
Series-Based Estimators
Higher Order Derivatives
Local Linear Estimators
Asymptotic Properties of Kernel-Based Average Derivative Estimators
Implementing the Derivative Estimators
Illustrative Examples
A Monte Carlo Experiment with a Production Function
Earnings-Age Relationship
Review of Applied Work
Semiparametric Estimation of Single-Equation Models
Introduction
Semiparametric Estimation of the Linear Part of a Regression Model
General Results
Diagnostic Tests after Nonparametric Regression
Semiparametric Estimation of Some Macro Models
The Asymptotic Covariance Matrix of SP Estimators without Asymptotic Independence
Efficient Estimation of Semiparametric Models in the Presence of Heteroskedasticity of Unknown Form
Conditions for Adaptive Estimation
Efficient Estimation of Regression Parameters with Unknown Error Density
Efficient Estimation by Likelihood Approximation
Efficient Estimation by Kernel-Based Score Approximation
Efficient Estimation by Moment-Based Score Approximation
Estimation of Scale Parameters
Optimal Diagnostic Tests in Linear Models
Adaptive Estimation with Dependent Observations
M-Estimators
Estimation
Diagnostic Tests with M-Estimators
Sequential M-Estimators
The Semiparametric Efficiency Bound for Moment-Based Estimators
Approximating the SP Efficiency Bound by a Conditional Moment Estimator
Applications
Semiparametric Estimation of a Heteroskedastic Model
Adaptive Estimation of a Model of House Prices
Review of Other Applications
Semiparametric and Nonparametric Estimation of Simultaneous Equation Models
Introduction
Single-Equation Estimators
Parametric Estimation
Rilstone's Semiparametric Two-Stage Least Squares Estimator
Systems Estimation
A Parametric Estimator
The SP3SLS Estimator
Newey's Estimator
Newey's Efficient Distribution-Free Estimators
Finite-Sample Properties
Nonparametric Estimation
Identification
Nonparametric Two-Stage Least Squares (2SLS) Estimation
Semiparametric Estimation of Discrete Choice Models
Introduction
Parametric Estimation of Binary Discrete Choice Models
Semiparametric Efficiency Bounds for Binary Discrete Choice Models
Semiparametric Estimation of Binary Discrete Choice Models
Ichimura's Estimator
Klein and Spady's Estimator
The SNP Maximum Likelihood Estimator
Local Maximum Likelihood Estimation
Alternative Consistent SP Estimators
Manski's Maximum Score Estimator
Horowitz's Smoothed Maximum Score Estimator
Han's Maximum Rank Correlation Estimator
Cosslett's Approximate MLE
An Iterative Least Squares Estimator
Derivative-Based Estimators
Models with Discrete Explanatory Variables
Multinomial Discrete Choice Models
Some Specification Tests for Discrete Choice Models
Applications
Semiparametric Estimation of Selectivity Models
Introduction
Some Parametric Estimators
Some Sequential Semiparametric Estimators
Cosslett's Dummy Variable Method
Powell's Kernel Estimator
Newey's Series Estimator
Newey's GMM Estimator
Maximum Likelihood-Type Estimators
Gallant and Nychka's Estimator
Newey's Estimator
Estimation of the Intercept in Selection Models
Applications of the Estimators
Conclusions
Semiparametric Estimation of Censored Regression Models
Introduction
Some Parametric Estimators
Semiparametric Efficiency Bounds for the Censored Regression Model
The Kaplan-Meier Estimator of the Distribution Function of a Censored Random Variable
Semiparametric Density-Based Estimators
The Semiparametric Generalized Least Squares Estimator (SGLS)
Estimators Replacing Part of the Sample
Maximum Likelihood Type Estimators
Semiparametric Nondensity-Based Estimators
Powell's Censored Least Absolute Deviation (CLAD) Estimator
Powell's (1986a) Censored Quantile Estimators
Powell's Symmetrically Censored Least Squares Estimators
Newey's Efficient Estimator under Conditional Symmetry
Comparative Studies of the Estimators
Retrospect and Prospect
Statistical Methods
Probability Concepts
Random Variable and Distribution Function
Conditional Distribution and Independence
Borel Measurable Functions
Inequalities Involving Expectations
Characteristic Function (c.f.)
Results on Convergence
Weak and Strong Convergence of Random Variables
Laws of Large Numbers
Convergence of Distribution Functions
Central Limit Theorems
Further Results on the Law of Large Numbers and Convergence in Moments and Distributions
Convergence in Moments
Some Probability Inequalities
Order of Magnitudes (Small o and Large O)
Asymptotic Theory for Dependent Observations
Ergodicity
Mixing Sequences
Near-Epoch Dependent Sequences
Martingale Differences and Mixingales
Rosenblatt's (1970) Measure of Dependence [beta][subscript n]
Stochastic Equicontinuity
References
Index

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