Muutke küpsiste eelistusi

Nonparametric Econometrics: Theory and Practice [Kõva köide]

4.33/5 (6 hinnangut Goodreads-ist)
,
  • Formaat: Hardback, 768 pages, kõrgus x laius: 254x178 mm, kaal: 1446 g, 23 b/w illus. 17 tables.
  • Ilmumisaeg: 17-Dec-2006
  • Kirjastus: Princeton University Press
  • ISBN-10: 0691121613
  • ISBN-13: 9780691121611
Teised raamatud teemal:
Nonparametric Econometrics: Theory and PracticeSuurem pilt
  • Formaat: Hardback, 768 pages, kõrgus x laius: 254x178 mm, kaal: 1446 g, 23 b/w illus. 17 tables.
  • Ilmumisaeg: 17-Dec-2006
  • Kirjastus: Princeton University Press
  • ISBN-10: 0691121613
  • ISBN-13: 9780691121611
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780691121611.html
Märksõnad:

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.

Arvustused

"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 "This book is ideal for a specialised graduate course. Li and Racine have done a fantastic job of bringing together all the latest developments in non-parametric estimation and treating them in a unified, accessible way. In particular, recent developments on using mixed continuous and discrete data, research to which Li and Raci have contributed immensely, are well covered."--Economic Record

Muu info

Nonparametric Econometrics by Li and Racine is a must for any serious econometrician or statistician who is working on cutting-edge problems. The theoretical treatment of nonparametric methods is remarkably complete in its coverage of mainstream and relatively arcane topics. I particularly like Li and Racine's general treatment of continuous and discrete regressors and of specification testing, topics that I have not seen handled in such a comprehensive fashion. I will certainly use this in my graduate econometrics courses and in conducting my own research. -- Robin Sickles, Rice University Very few studies have tried to apply the nonparametric techniques to analyze real data. The lack of applications of those techniques is perhaps attributable to the lack of a good textbook that explains intuitively how and why those techniques work. This book by Li and Racine serves both applied researchers and graduate students. It is written in plain language so that it can be understood by anyone with basic econometrics but zero knowledge of nonparametric methods. And it contains enough specifics that clearly spell out steps to implement those methods. -- Chunrong Ai, University of Florida This book represents a very significant contribution to the field of econometrics. It provides an extremely thorough coverage of our knowledge in the area of nonparametric and semiparametric methods as they apply to economic models and economic data. And it makes accessible, for the first time, a body of relatively new material relating to discrete and 'mixed' data. There is a good balance of theoretical material and applications. Apart from serving as a superb teaching text in graduate-level courses where the students have a strong econometrics/statistics preparation, I believe this book will become a must-have reference resource for many researchers. -- David E. Giles, University of Victoria
Preface xvii
I Nonparametric Kernel Methods
1(218)
Density Estimation
3(54)
Univariate Density Estimation
4(10)
Univariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods
14(1)
Univariate Bandwidth Selection: Cross-Validation Methods
15(4)
Least Squares Cross-Validation
15(3)
Likelihood Cross-Validation
18(1)
An Illustration of Data-Driven Bandwidth Selection
19(1)
Univariate CDF Estimation
19(4)
Univariate CDF Bandwidth Selection: Cross-Validation Methods
23(1)
Multivariate Density Estimation
24(2)
Multivariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods
26(1)
Multivariate Bandwidth Selection: Cross-Validation Methods
27(1)
Least Squares Cross-Validation
27(1)
Likelihood Cross-Validation
28(1)
Asymptotic Normality of Density Estimators
28(2)
Uniform Rates of Convergence
30(3)
Higher Order Kernel Functions
33(2)
Proof of Theorem 1.4 (Uniform Almost Sure Convergence)
35(5)
Applications
40(7)
Female Wage Inequality
41(2)
Unemployment Rates and City Size
43(1)
Adolescent Growth
44(1)
Old Faithful Geyser Data
44(1)
Evolution of Real Income Distribution in Italy, 1951--1998
45(2)
Exercises
47(10)
Regression
57(58)
Local Constant Kernel Estimation
60(6)
Intuition Underlying the Local Constant Kernel Estimator
64(2)
Local Constant Bandwidth Selection
66(12)
Rule-of-Thumb and Plug-In Methods
66(3)
Least Squares Cross-Validation
69(3)
AICc
72(1)
The Presence of Irrelevant Regressors
73(5)
Some Further Results on Cross-Validation
78(1)
Uniform Rates of Convergence
78(1)
Local Linear Kernel Estimation
79(6)
Local Linear Bandwidth Selection: Least Squares Cross-Validation
83(2)
Local Polynomial Regression (General pth Order)
85(7)
The Univariate Case
85(3)
The Multivariate Case
88(1)
Asymptotic Normality of Local Polynomial Estimators
89(3)
Applications
92(5)
Prestige Data
92(1)
Adolescent Growth
92(1)
Inflation Forecasting and Money Growth
93(4)
Proofs
97(11)
Derivation of (2.24)
98(2)
Proof of Theorem 2.7
100(6)
Definitions of Al,p+1 and Vl Used in Theorem 2.10
106(2)
Exercises
108(7)
Frequency Estimation with Mixed Data
115(10)
Probability Function Estimation with Discrete Data
116(2)
Regression with Discrete Regressors
118(1)
Estimation with Mixed Data: The Frequency Approach
118(2)
Density Estimation with Mixed Data
118(1)
Regression with Mixed Data
119(1)
Some Cautionary Remarks on Frequency Methods
120(2)
Proofs
122(1)
Proof of Theorem 3.1
122(1)
Exercises
123(2)
Kernel Estimation with Mixed Data
125(30)
Smooth Estimation of Joint Distributions with Discrete Data
126(5)
Smooth Regression with Discrete Data
131(3)
Kernel Regression with Discrete Regressors: The Irrelevant Regressor Case
134(2)
Regression with Mixed Data: Relevant Regressors
136(4)
Smooth Estimation with Mixed Data
136(2)
The Cross-Validation Method
138(2)
Regression with Mixed Data: Irrelevant Regressors
140(5)
Ordered Discrete Variables
144(1)
Applications
145(5)
Food-Away-from-Home Expenditure
145(2)
Modeling Strike Volume
147(3)
Exercises
150(5)
Conditional Density Estimation
155(26)
Conditional Density Estimation: Relevant Variables
155(2)
Conditional Density Bandwidth Selection
157(5)
Least Squares Cross-Validation: Relevant Variables
157(3)
Maximum Likelihood Cross-Validation: Relevant Variables
160(2)
Conditional Density Estimation: Irrelevant Variables
162(2)
The Multivariate Dependent Variables Case
164(7)
The General Categorical Data Case
167(1)
Proof of Theorem 5.5
168(3)
Applications
171(9)
A Nonparametric Analysis of Corruption
171(1)
Extramarital Affairs Data
172(3)
Married Female Labor Force Participation
175(2)
Labor Productivity
177(1)
Multivariate Y Conditional Density Example: GDP Growth and Population Growth Conditional on OECD Status
178(2)
Exercises
180(1)
Conditional CDF and Quantile Estimation
181(38)
Estimating a Conditional CDF with Continuous Covariates without Smoothing the Dependent Variable
182(2)
Estimating a Conditional CDF with Continuous Covariates Smoothing the Dependent Variable
184(5)
Nonparametric Estimation of Conditional Quantile Functions
189(2)
The Check Function Approach
191(2)
Conditional CDF and Quantile Estimation with Mixed Discrete and Continuous Covariates
193(3)
A Small Monte Carlo Simulation Study
196(2)
Nonparametric Estimation of Hazard Functions
198(2)
Applications
200(9)
Boston Housing Data
200(2)
Adolescent Growth Charts
202(1)
Conditional Value at Risk
202(4)
Real Income in Italy, 1951--1998
206(1)
Multivariate Y Conditional CDF Example: GDP Growth and Population Growth Conditional on OECD Status
206(3)
Proofs
209(6)
Proofs of Theorems 6.1, 6.2, and 6.4
209(5)
Proofs of Theorems 6.5 and 6.6 (Mixed Covariates Case)
214(1)
Exercises
215(4)
II Semiparametric Methods
219(130)
Semiparametric Partially Linear Models
221(28)
Partially Linear Models
222(1)
Identification of β
222(1)
Robinson's Estimator
222(8)
Estimation of the Nonparametric Component
228(2)
Andrews's MINPIN Method
230(3)
Semiparametric Efficiency Bounds
233(5)
The Conditionally Homoskedastic Error Case
233(2)
The Conditionally Heteroskedastic Error Case
235(3)
Proofs
238(8)
Proofs of Theorem 7.2
238(6)
Verifying Theorem 7.3 for a Partially Linear Model
244(2)
Exercises
246(3)
Semiparametric Single Index Models
249(34)
Identification Conditions
251(2)
Estimation
253(5)
Ichimura's Method
253(5)
Direct Semiparametric Estimators for β
258(5)
Average Derivative Estimators
258(4)
Estimation of g(.)
262(1)
Bandwidth Selection
263(3)
Bandwidth Selection for Ichimura's Method
263(2)
Bandwidth Selection with Direct Estimation Methods
265(1)
Klein and Spady's Estimator
266(1)
Lewbel's Estimator
267(2)
Manski's Maximum Score Estimator
269(1)
Horowitz's Smoothed Maximum Score Estimator
270(1)
Han's Maximum Rank Estimator
270(1)
Multinomial Discrete Choice Models
271(1)
Ai's Semiparametric Maximum Likelihood Approach
272(3)
A Sketch of the Proof of Theorem 8.1
275(2)
Applications
277(4)
Modeling Response to Direct Marketing Catalog Mailings
277(4)
Exercises
281(2)
Additive and Smooth (Varying) Coefficient Semiparametric Models
283(32)
An Additive Model
283(14)
The Marginal Integration Method
284(2)
A Computationally Efficient Oracle Estimator
286(3)
The Ordinary Backfitting Method
289(1)
The Smoothed Backfitting Method
290(5)
Additive Models with Link Functions
295(2)
An Additive Partially Linear Model
297(4)
A Simple Two-Step Method
299(2)
A Semiparametric Varying (Smooth) Coefficient Model
301(11)
A Local Constant Estimator of the Smooth Coefficient Function
302(1)
A Local Linear Estimator of the Smooth Coefficient Function
303(3)
Testing for a Parametric Smooth Coefficient Model
306(2)
Partially Linear Smooth Coefficient Models
308(2)
Proof of Theorem 9.3
310(2)
Exercises
312(3)
Selectivity Models
315(16)
Semiparametric Type-2 Tobit Models
316(1)
Estimation of a Semiparametric Type-2 Tobit Model
317(3)
Gallant and Nychka's Estimator
318(1)
Estimation of the Intercept in Selection Models
319(1)
Semiparametric Type-3 Tobit Models
320(8)
Econometric Preliminaries
320(3)
Alternative Estimation Methods
323(5)
Das, Newey and Vella's Nonparametric Selection Model
328(2)
Exercises
330(1)
Censored Models
331(18)
Parametric Censored Models
332(2)
Semiparametric Censored Regression Models
334(2)
Semiparametric Censored Regression Models with Nonparametric Heteroskedasticity
336(2)
The Univariate Kaplan-Meier CDF Estimator
338(3)
The Multivariate Kaplan-Meier CDF Estimator
341(4)
Nonparametric Regression Models with Random Censoring
343(2)
Nonparametric Censored Regression
345(3)
Lewbel and Linton's Approach
345(1)
Chen, Dahl and Khan's Approach
346(2)
Exercises
348(1)
III Consistent Model Specification Tests
349(64)
Model Specification Tests
351(46)
A Simple Consistent Test for Parametric Regression Functional Form
354(8)
A Consistent Test for Correct Parametric Functional Form
355(5)
Mixed Data
360(2)
Testing for Equality of PDFs
362(3)
More Tests Related to Regression Functions
365(13)
Hardle and Mammen's Test for a Parametric Regression Model
365(2)
An Adaptive and Rate Optimal Test
367(2)
A Test for a Parametric Single Index Model
369(1)
A Nonparametric Omitted Variables Test
370(5)
Testing the Significance of Categorical Variables
375(3)
Tests Related to PDFs
378(7)
Testing Independence between Two Random Variables
378(2)
A Test for a Parametric PDF
380(2)
A Kernel Test for Conditional Parametric Distributions
382(3)
Applications
385(3)
Growth Convergence Clubs
385(3)
Proofs
388(6)
Proof of Theorem 12.1
388(1)
Proof of Theorem 12.2
389(1)
Proof of Theorem 12.5
389(2)
Proof of Theorem 12.9
391(3)
Exercises
394(3)
Nonsmoothing Tests
397(16)
Testing for Parametric Regression Functional Form
398(3)
Testing for Equality of PDFs
401(1)
A Nonparametric Significance Test
401(1)
Andrews's Test for Conditional CDFs
402(2)
Hong's Tests for Serial Dependence
404(4)
More on Nonsmoothing Tests
408(1)
Proofs
409(1)
Proof of Theorem 13.1
409(1)
Exercises
410(3)
IV Nonparametric Nearest Neighbor and Series Methods
413(90)
K-Nearest Neighbor Methods
415(30)
Density Estimation: The Univariate Case
415(4)
Regression Function Estimation
419(2)
A Local Linear k-nn Estimator
421(1)
Cross-Validation with Local Constant k-nn Estimation
422(3)
Cross-Validation with Local Linear k-nn Estimation
425(2)
Estimation of Semiparametric Models with k-nn Methods
427(1)
Model Specification Tests with K-nn Methods
428(4)
A Bootstrap Test
431(1)
Using Different k for Different Components of x
432(1)
Proofs
432(12)
Proof of Theorem 14.1
435(1)
Proof of Theorem 14.5
435(5)
Proof of Theorem 14.10
440(4)
Exercises
444(1)
Nonparametric Series Methods
445(58)
Estimating Regression Functions
446(5)
Convergence Rates
449(2)
Selection of the Series Term K
451(3)
Asymptotic Normality
453(1)
A Partially Linear Model
454(12)
An Additive Partially Linear Model
455(6)
Selection of Nonlinear Additive Components
461(2)
Estimating an Additive Model with a Known Link Function
463(3)
Estimation of Partially Linear Varying Coefficient Models
466(13)
Testing for Correct Parametric Regression Functional Form
471(3)
A Consistent Test for an Additive Partially Linear Model
474(5)
Other Series-Based Tests
479(1)
Proofs
480(22)
Proof of Theorem 15.1
480(4)
Proof of Theorem 15.3
484(4)
Proof of Theorem 15.6
488(4)
Proof of Theorem 15.9
492(5)
Proof of Theorem 15.10
497(5)
Exercises
502(1)
V Time Series, Simultaneous Equation, and Panel Data Models
503(160)
Instrumental Variables and Efficient Estimation of Semiparametric Models
505(16)
A Partially Linear Model with Endogenous Regressors in the Parametric Part
505(4)
A Varying Coefficient Model with Endogenous Regressors in the Parametric Part
509(2)
Ai and Chen's Efficient Estimator with Conditional Moment Restrictions
511(6)
Estimation Procedures
511(2)
Asymptotic Normality for Ø
513(2)
A Partially Linear Model with the Endogenous Regressors in the Nonparametric Part
515(2)
Proof of Equation (16.16)
517(3)
Exercises
520(1)
Endogeneity in Nonparametric Regression Models
521(14)
A Nonparametric Model
521(1)
A Triangular Simultaneous Equation Model
522(5)
Newey-Powell Series-Based Estimator
527(2)
Hall and Horowitz's Kernel-Based Estimator
529(3)
Darolles, Florens and Renault's Estimator
532(1)
Exercises
533(2)
Weakly Dependent Data
535(40)
Density Estimation with Dependent Data
537(4)
Uniform Almost Sure Rate of Convergence
541(1)
Regression Models with Dependent Data
541(10)
The Martingale Difference Error Case
541(3)
The Autocorrelated Error Case
544(2)
One-Step-Ahead Forecasting
546(1)
d-Step-Ahead Forecasting
547(1)
Estimation of Nonparametric Impulse Response Functions
548(3)
Semiparametric Models with Dependent Data
551(3)
A Partially Linear Model with Dependent Data
551(1)
Additive Regression Models
552(1)
Varying Coefficient Models with Dependent Data
553(1)
Testing for Serial Correlation in Semiparametric Models
554(2)
The Test Statistic and Its Asymptotic Distribution
554(1)
Testing Zero First Order Serial Correlation
555(1)
Model Specification Tests with Dependent Data
556(2)
A Kernel Test for Correct Parametric Regression Functional Form
556(1)
Nonparametric Significance Tests
557(1)
Nonsmoothing Tests for Regression Functional Form
558(1)
Testing Parametric Predictive Models
559(5)
In-Sample Testing of Conditional CDFs
559(3)
Out-of-Sample Testing of Conditional CDFs
562(2)
Applications
564(2)
Forecasting Short-Term Interest Rates
564(2)
Nonparametric Estimation with Nonstationary Data
566(1)
Proofs
567(5)
Proof of Equation (18.9)
567(2)
Proof of Theorem 18.2
569(3)
Exercises
572(3)
Panel Data Models
575(52)
Nonparametric Estimation of Panel Data Models: Ignoring the Variance Structure
576(2)
Wang's Efficient Nonparametric Panel Data Estimator
578(6)
A Partially Linear Model with Random Effects
584(2)
Nonparametric Panel Data Models with Fixed Effects
586(6)
Error Variance Structure Is Known
587(3)
The Error Variance Structure Is Unknown
590(2)
A Partially Linear Model with Fixed Effects
592(2)
Semiparametric Instrumental Variable Estimators
594(5)
An Infeasible Estimator
594(1)
The Choice of Instruments
595(2)
A Feasible Estimator
597(2)
Testing for Serial Correlation and for Individual Effects in Semiparametric Models
599(3)
Series Estimation of Panel Data Models
602(4)
Additive Effects
602(2)
Alternative Formulation of Fixed Effects
604(2)
Nonlinear Panel Data Models
606(12)
Censored Panel Data Models
607(7)
Discrete Choice Panel Data Models
614(4)
Proofs
618(6)
Proof of Theorem 19.1
618(3)
Leading MSE Calculation of Wang's Estimator
621(3)
Exercises
624(3)
Topics in Applied Nonparametric Estimation
627(36)
Nonparametric Methods in Continuous-Time Models
627(12)
Nonparametric Estimation of Continuous-Time Models
627(5)
Nonparametric Tests for Continuous-Time Models
632(1)
Ait-Sahalia's Test
632(1)
Hong and Li's Test
633(3)
Proofs
636(3)
Nonparametric Estimation of Average Treatment Effects
639(6)
The Model
640(2)
An Application: Assessing the Efficacy of Right Heart Catheterization
642(3)
Nonparametric Estimation of Auction Models
645(6)
Estimation of First Price Auction Models
645(3)
Conditionally Independent Private Information Auctions
648(3)
Copula-Based Semiparametric Estimation of Multivariate Distributions
651(8)
Some Background on Copula Functions
651(1)
Semiparametric Copula-Based Multivariate Distributions
652(1)
A Two-Step Estimation Procedure
653(2)
A One-Step Efficient Estimation Procedure
655(2)
Testing Parametric Functional Forms of a Copula
657(2)
A Semiparametric Transformation Model
659(3)
Exercises
662(1)
A Background Statistical Concepts
663(34)
Probability, Measure, and Measurable Space
663(9)
Metric, Norm, and Functional Spaces
672(8)
Limits and Modes of Convergence
680(8)
Limit Supremum and Limit Infimum
680(1)
Modes of Convergence
681(7)
Inequalities, Laws of Large Numbers, and Central Limit Theorems
688(6)
Exercises
694(3)
Bibliography 697(40)
Author Index 737(7)
Subject Index 744


Qi Li is Professor of Economics and Hugh Roy Cullen Professor in Liberal Arts at Texas A&M University. Jeffrey Scott Racine is Professor of Economics, Professor in the Graduate Program in Statistics, and Senator William McMaster Chair in Econometrics at McMaster University.