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E-raamat: Introduction to the Advanced Theory and Practice of Nonparametric Econometrics: A Replicable Approach Using R

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(McMaster University, Ontario)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Jun-2019
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108757287
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Introduction to the Advanced Theory and Practice of Nonparametric Econometrics: A Replicable Approach Using RSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Jun-2019
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108757287
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Interest in nonparametric methodology has grown considerably over the past few decades, stemming in part from vast improvements in computer hardware and the availability of new software that allows practitioners to take full advantage of these numerically intensive methods. This book is written for advanced undergraduate students, intermediate graduate students, and faculty, and provides a complete teaching and learning course at a more accessible level of theoretical rigor than Racine's earlier book co-authored with Qi Li, Nonparametric Econometrics: Theory and Practice (2007). The open source R platform for statistical computing and graphics is used throughout in conjunction with the R package np. Recent developments in reproducible research is emphasized throughout with appendices devoted to helping the reader get up to speed with R, R Markdown, TeX and Git.

Arvustused

'This book will be valuable to economists wishing to learn nonparametric methods, and to practitioners needing the details of implementation. Applied economists will find this an excellent and practical reference guide.' Bruce E. Hansen, University of Wisconsin, Madison 'This book manages to be comprehensive, careful, and accessible all at once - an impressive achievement for such a challenging subject. It covers topics not found elsewhere and incorporates them in a systematic, unified approach. Illustrations using the R programming language will have broad appeal for both teachers and users of nonparametric methods.' Jeffrey M. Woolridge, Michigan State University

Muu info

Provides theory, open source R implementations, and the latest tools for reproducible nonparametric econometric research.
List of Tables xii
List of Figures xiv
Preface xix
Glossary of Notation xxv
I Probability Functions, Probability Density Functions, and Their Cumulative Counterparts 1(186)
1 Discrete Probability and Cumulative Probability Functions
3(46)
1.1 Overview
3(2)
1.2 Parametric Probability Function Estimation
5(3)
1.3 Nonsmooth Probability Function Estimation
8(3)
1.4 Smooth Kernel Probability Function Estimation
11(11)
1.4.1 Estimator Properties for Unordered Categorical Variables and Kernels
12(4)
1.4.2 The SMSE-Optimal Smoothing Parameter and Rate of Convergence
16(2)
1.4.3 Asymptotic Normality
18(1)
1.4.4 Kernel Estimation and Shrinkage
18(1)
1.4.5 Estimator Properties for Ordered Categorical Variables and Kernels
19(3)
1.5 Nonsmooth Cumulative Probability Function Estimation
22(3)
1.6 Smooth Kernel Cumulative Probability Function Estimation
25(2)
1.7 The Multivariate Extension
27(2)
1.8 Practitioner's Corner
29(16)
1.8.1 Estimating Probability Functions in R
29(5)
1.8.2 A Monte Carlo Comparison of Probability Estimators
34(11)
Problem Set
45(4)
2 Continuous Density and Cumulative Distribution Functions
49(82)
2.1 Overview
49(1)
2.2 Parametric Density Function Estimation
50(1)
2.3 Nonsmooth Density Function Estimation
51(5)
2.3.1 The Histogram Density Estimator
51(1)
2.3.2 The Naive Density Estimator
52(4)
2.4 Smooth Kernel Density Function Estimation
56(21)
2.4.1 Properties of the Rosenblatt-Parzen Kernel Density Estimator
58(8)
2.4.2 The IMSE-Optimal Bandwidth and Rate of Convergence
66(1)
2.4.3 The IMSE-Optimal Kernel Function
67(2)
2.4.4 Asymptotic Normality
69(2)
2.4.5 Bandwidth Selection
71(4)
2.4.6 Bias-Reducing Kernel Functions
75(2)
2.5 Smooth Kernel Cumulative Distribution Function Estimation
77(5)
2.5.1 Properties of the Kernel Cumulative Distribution Function Estimator
77(3)
2.5.2 IMSE-Optimal Bandwidth
80(1)
2.5.3 Asymptotic Normality
81(1)
2.5.4 Bandwidth Selection
81(1)
2.6 Smooth Kernel Quantile Function Estimation
82(3)
2.7 The Multivariate Extension
85(4)
2.7.1 Properties of the Multivariate Kernel Density Estimator
87(1)
2.7.2 Properties of the Multivariate Kernel Cumulative Distribution Function Estimator
88(1)
2.8 Entropy and Information Measures
89(8)
2.8.1 Statistical Mechanics and Information Functions
89(2)
2.8.2 Relative Entropy
91(2)
2.8.3 Joint and Conditional Entropy
93(1)
2.8.4 Mutual Information
93(1)
2.8.5 Entropy and Metricness
94(1)
2.8.6 Entropy and Axiom Systems
94(1)
2.8.7 Entropy, Inference, Robustness, and Consistency
95(1)
2.8.8 Kernel Estimation and Entropy
96(1)
2.9 Practitioner's Corner
97(32)
2.9.1 The Smoothed Bootstrap
103(1)
2.9.2 Testing Univariate Asymmetry
104(2)
2.9.3 Testing Equality of Univariate Densities
106(2)
2.9.4 Testing Nonlinear Pairwise Independence
108(1)
2.9.5 Testing Nonlinear Serial Independence
109(2)
2.9.6 Bounded Domains and Boundary Corrections
111(7)
2.9.7 Nonlinear Optimization and Multi-Starting
118(5)
2.9.8 Confidence Bands and Nonparametric Estimation
123(6)
Problem Set
129(2)
3 Mixed-Data Probability Density and Cumulative Distribution Functions
131(16)
3.1 Overview
131(1)
3.2 Smooth Mixed-Data Kernel Density and Cumulative Distribution Function Estimation
132(3)
3.2.1 Properties of the Mixed-Data Smooth Kernel Density Estimator
133(2)
3.2.2 Properties of the Mixed-Data Smooth Kernel Cumulative Distribution Estimator
135(1)
3.3 The Multivariate Extension
135(2)
3.4 Smooth Kernel Copula Function Estimation with Mixed-Data
137(5)
3.4.1 Copulae and Dependence
139(3)
3.5 Practitioner's Corner
142(3)
3.5.1 Testing Equality of Mixed-Data Multivariate Densities
142(1)
3.5.2 Generating Copula Function Contours
143(2)
Problem Set
145(2)
4 Conditional Probability Density and Cumulative Distribution Functions
147(40)
4.1 Overview
147(1)
4.2 Smooth Kernel Conditional Density Function Estimation
148(4)
4.2.1 Bandwidth Selection
149(1)
4.2.2 The Presence of Irrelevant Covariates
150(2)
4.3 Smooth Kernel Conditional Cumulative Distribution Function Estimation
152(2)
4.3.1 Bandwidth Selection
153(1)
4.4 Conditional Quantile Function Estimation
154(4)
4.4.1 Parametric Conditional Quantile Function Estimation
154(3)
4.4.2 Smooth Kernel Conditional Quantile Function Estimation
157(1)
4.5 Binary Choice and Multinomial Choice Models
158(4)
4.5.1 Parametric Binary Choice and Multinomial Choice Models
158(1)
4.5.2 Smooth Kernel Binary Choice and Multinomial Choice Models
159(3)
4.6 Practitioner's Corner
162(23)
4.6.1 Generating Counterfactual Predictions
166(1)
4.6.2 Bootstrapping Counterfactual Predictions
166(4)
4.6.3 The Smoothed Bootstrap
170(2)
4.6.4 Assessing Model Performance
172(7)
4.6.5 Average Treatment Effects and Propensity Score Matching
179(6)
Problem Set
185(2)
II Conditional Moment Functions and Related Statistical Objects 187(128)
5 Conditional Moment Functions
189(4)
5.1 Overview
189(4)
6 Conditional Mean Function Estimation
193(82)
6.1 Overview
193(2)
6.2 Parametric Conditional Mean Models
195(11)
6.2.1 (Re)-interpretation of Conditional Mean Models
197(2)
6.2.2 Counterfactual Experiments and Conditional Mean Models
199(7)
6.3 Local Constant Kernel Regression
206(20)
6.3.1 Estimator Properties
208(10)
6.3.2 The IMSE-Optimal Bandwidth and Kernel Function
218(1)
6.3.3 Asymptotic Normality
219(1)
6.3.4 Outlier-Resistant Local Constant Kernel Regression
219(1)
6.3.5 Bandwidth Selection
220(2)
6.3.6 A Coefficient of Determination for Nonparametric Regression
222(1)
6.3.7 Local Constant Marginal Effects
223(3)
6.4 Local Polynomial Kernel Regression
226(3)
6.5 The Multivariate Local Polynomial Extension
229(3)
6.6 Local Polynomial Kernel Regression and Shrinkage
232(3)
6.7 Multivariate Mixed-Data Marginal Effects
235(5)
6.7.1 A Consistent Test for Predictor Relevance
236(4)
6.8 Time Series Kernel Regression
240(5)
6.9 Shape Constrained Kernel Regression
245(3)
6.10 Practitioner's Corner
248(25)
6.10.1 Kernel Regression Is Weighted Least Squares Estimation
248(1)
6.10.2 Joint Determination of the Polynomial Degree and Bandwidth
249(4)
6.10.3 A Consistent Nonparametric Test for Correct Parametric Specification
253(4)
6.10.4 Shape Constrained Kernel Regression
257(3)
6.10.5 A Multivariate Application of Local Linear Regression
260(3)
6.10.6 Confidence Bands and Nonparametric Estimation
263(1)
6.10.7 Assessing Model Performance
264(5)
6.10.8 Fixed-Effects Panel Data Models
269(4)
Problem Set
273(2)
7 Conditional Mean Function Estimation with Endogenous Predictors
275(16)
7.1 Overview
275(1)
7.2 Ill-Posed Inverse Problems and Identification
276(4)
7.2.1 Kernel Smoothing and Ill-Posedness
277(2)
7.2.2 Singular Design Matrices and Ill-Posedness
279(1)
7.3 Parametric Instrumental Regression
280(1)
7.4 Nonparametric Instrumental Regression
281(4)
7.5 Practitioner's Corner
285(4)
7.5.1 Estimation of Engel Curves
285(1)
7.5.2 Nonparametric Instrumental Regression with a Linear DGP
285(4)
Problem Set
289(2)
8 Semiparametric Conditional Mean Function Estimation
291(18)
8.1 Overview
291(1)
8.2 Robinson's Partially Linear Model
291(3)
8.3 Varying Coefficient Models
294(2)
8.4 Semiparametric Single Index Models
296(4)
8.4.1 Ichimura's Method (Continuous Y)
297(1)
8.4.2 Klein and Spady's Method (Binary Y)
298(2)
8.5 Summary
300(1)
8.6 Practitioner's Corner
300(7)
8.6.1 A Specification Test for the Partially Linear Model
300(1)
8.6.2 Assessing Model Performance - Continuous Y
301(6)
Problem Set
307(2)
9 Conditional Variance Function Estimation
309(6)
9.1 Overview
309(1)
9.2 Local Linear Conditional Variance Function Estimation
309(2)
9.3 Practitioner's Corner
311(2)
9.3.1 A Simulated Illustration
311(2)
Problem Set
313(2)
III Appendices 315(52)
A Large and Small Orders of Magnitude and Probability
317(6)
A.1 Big and Small O Notation
317(2)
A.2 Big and Small O in Probability Notation
319(4)
B R, RStudio, TeX, and Git
323(4)
B.1 Installation of R and RStudio Desktop
323(1)
B.2 What Is R?
323(2)
B.2.1 R in the News
324(1)
B.2.2 Introduction to R
324(1)
B.2.3 Econometrics in R
324(1)
B.3 What Is RStudio Desktop?
325(1)
B.3.1 Introduction to RStudio
325(1)
B.4 Installation of TeX
325(1)
B.5 Installation of Git
325(2)
C Computational Considerations
327(6)
C.1 Binning Methods
327(1)
C.2 Transforms
328(1)
C.3 Parallelism
328(1)
C.4 Multipole and Tree-Based Methods
328(1)
C.5 Computationally Efficient Kernel Estimation in R
328(5)
D R Markdown for Assignments
333(10)
D.1 Source Code (R Markdown) for This Document
333(1)
D.2 R, RStudio, TeX, and Git
333(1)
D.3 What Is R Markdown?
333(1)
D.4 Creating a New R Markdown Document in RStudio
334(1)
D.5 Including R Results in Your R Markdown Document
334(1)
D.6 Reading Data from a URL
334(1)
D.7 Including Plots
335(1)
D.8 Including Bulleted and Numbered Lists
336(1)
D.9 Including Tables
337(1)
D.10 Including Verbatim (i.e., Freeform) Text
337(1)
D.11 Typesetting Mathematics
337(1)
D.12 Flexible Document Creation
338(1)
D.13 Knitting Your R Markdown Document
338(1)
D.14 Printing Your Document
338(1)
D.15 Troubleshooting and Tips
339(4)
E Practicum
343(24)
E.1 Overview
343(1)
E.2 Getting Started with R
343(3)
E.2.1 Reading Datasets Created by Other Software Programs
344(1)
E.2.2 Nonparametric Estimation of Density Functions
345(1)
E.3 Introduction to the R Package np: Working with npudens()
346(21)
E.3.1 Introduction to the npksum() Function
348(1)
E.3.2 Applied Nonparametric Density Estimation
349(2)
E.3.3 Introduction to Applied Nonparametric Regression
351(1)
E.3.4 Advanced Use of the npksum() Function
352(2)
E.3.5 Consistent Nonparametric Inference
354(3)
E.3.6 Non-nested Model Comparison
357(2)
E.3.7 Semiparametric Models
359(1)
E.3.8 Nonparametric Discrete Choice Models
360(2)
E.3.9 Shape Constrained Nonparametric Regression
362(5)
Bibliography 367(24)
Author Index 391(6)
Subject Index 397
Jeffrey S. Racine is Professor in the Department of Economics and Professor in the Graduate Program in Statistics in the Department of Mathematics and Statistics at McMaster University, Ontario. He holds the Senator William McMaster Chair in Econometrics and is a Fellow of the Journal of Econometrics. He is co-author of Nonparametric Econometrics: Theory and Practice (2007). He has published extensively in his field and has co-authored the R packages np and crs that are available on the Comprehensive R Archive Network (CRAN).
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