An introduction to the advanced theory of nonparametric econometrics: a replicable approach using R
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2019
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxvi, 408 Seiten Illustrationen, Diagramme |
| ISBN: | 9781108483407 1108483402 |
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| adam_text | Contents List of Tables xii List of Figures xiv Preface xix Glossary of Notation xxv I Probability Functions, Probability Density Func tions, and Their CumulativeCounterparts 1 Discrete Probability and Cumulative Probability Func tions 1.1 Overview 1.2 Parametric Probability Function Estimation 1.3 Nonsmooth Probability Function Estimation 1.4 Smooth Kernel Probability Function Estimation 1.4.1 Estimator Properties for Unordered Categorical Vari ables and Kernels 1.4.2 The SMSE-Optimal Smoothing Parameter and Rate of Convergence 1.4.3 Asymptotic Normality 1.4.4 Kernel Estimation and Shrinkage 1.4.5 Estimator Properties for Ordered Categorical Vari ables and Kernels 1.5 Nonsmooth Cumulative Probability Function Estimation 1.6 Smooth Kernel Cumulative Probability Function Estimation 1.7 The Multivariate Extension 1.8 Practitioner’s Corner 1.8.1 Estimating Probability Functions in R 1.8.2 A Monte CarloComparison ofProbability Estimators Problem Set 1 3 3 5 8 11 12 16 18 18 19 22 25 27 29 29 34 45 v
VI CONTEXTS 2 Continuous Density and Cumulative Distribution Funetions 2.1 Overview 2.2 Parametric՜ Density Function list imat ion 2.8 Nonsmooth Density Function Estimation 2.8.1 The Histogram Density Estimator 2.8.2 The Naïve; Density Estimator 2.4 Smooth Kernel Density Function Estimation 2.4.1 Properties of the՝ Roseniblat f-Parzeni Кетиеі Density Estimator 2.4.2 The IMSE-Optimal Bandwidth and Rate of Conver gence; 2.4.8 The IMSE-Optimal Кетлеі Function 2.4.4 Asymptotic Normality 2.4.5 Bandwidth Selection 2.4.G Bias-Reducing Kernel Functions 2. Г) Smooth Kernel Cumulative Distribution Function Estimation 2.5.1 Properties of the Keane! Cumulative Distribution Function Estimator 2.5.2 IMSE-Optimal Baml width 2.5.8 Asymptotic Normality 2.5.4 Bandwidth. Scdc. ction 2.G Smooth Kernel Quantile՝ Function Estimation 2.7 The Multi variate՝ Extension 2.7.1 Properties of the Mult ivariate՝ Kernel Demsity Esti mate)]՜ 2.7.2 Properties of the; Multivariate Kerned Cumulative Distribut ion Funel ion Estimator 2.8 Entropy and Information Measure s 2.8.1 Statistical Mechanics and Information Functions 2.8.2 Relative; Entropy 2.8.8 Joint, and Conditional Entropy 2.8.4 Mutual Information 2.8.5 Entropy and Metricness 2.8.G Ent.rejpy and Axiom Systenns 2.8.7 Entropy, Infereaice;, Robustness, and Consistency 2.8.8 Kernel Estimation and Entropy 2.9 Practitioner’s Corner 2.9.1 The; Smoothed Bootstrap 2.9.2 Jesting Univariate Asymmelry 2.9.8 Testing Equality of Univariate Densities 2.9.4 Jesting Nonlinear Pairwise; Independence 2.9.5 Jesting Nemliiu ar Seniai Independence 49 49 50 51 51 52 5G
58 GG 67 G9 71 75 77 77 80 81 81 82 85 87 88 89 89 91 98 98 94 94 95 9G 97 103 104 10G 108 109
CONTENTS 2.9.6 2.9.7 2.9.8 Problem Set 3 Bounded Domains and Boundary Corrections Nonlinear Optimization and Multi-Starting Confidence Bands and Nonparametric Estimation 111 118 123 129 Mixed-Data Probability Density and Cumulative Distri bution Functions 131 3.1 Overview 131 3.2 Smooth Mixed-Data Kernel Density and Cumulative Dis tribution Function Estimation 132 3.2.1 Properties of the Mixed-Data Smooth Kernel Density Estimator 133 3.2.2 Properties of the Mixed-Data Smooth Kernel Cumu lative Distribution Estimator 135 3.3 The Multivariate Extension 135 3.4 Smooth Kernel Copula Function Estimation with Mixed-Data 137 3.4.1 Copulae and Dependence 139 3.5 Practitioner’s Corner 142 3.5.1 Testing Equality of Mixed-Data Multivariate Densities 142 3.5.2 Generating Copula Function Contours 143 Problem Set 4 vii 145 Conditional Probability Density and Cumulative Distri bution Functions 147 4.1 Overview 147 4.2 Smooth Kernel Conditional Density Function Estimation 148 4.2.1 Bandwidth Selection 149 4.2.2 The Presence of Irrelevant Covariates 150 4.3 Smooth Kernel Conditional Cumulative Distribution Func tion Estimation 152 4.3.1 Bandwidth Selection 153 4.4 Conditional Quantile Function Estimation 154 4.4.1 Parametric Conditional Quantile Function Estimation 154 4.4.2 Smooth Kernel Conditional Quantile Function Esti mation 157 4.5 Binary Choice and Multinomial Choice Models 158 4.5.1 Parametric Binary Choice and Multinomial Choice Models 158 4.5.2 Smooth Kernel Binary Choice and Multinomial Choice Models 159 4.6 Practitioner’s Corner 162 4.6.1 Generating Counterfactual
Predictions 166
viii CONTENTS 4.6.2 4.6.3 4.6.4 4.6.5 Bootstrapping Counterfactual Predictions The Smoothed Bootstrap Assessing Model Performance Average Treatment Effects and Propensity Score Matching 166 170 172 179 Problem Set 185 II Conditional Moment Functions and Related Statis tical Objects 187 5 6 Conditional Moment Functions 189 5.1 189 Overview , Conditional Mean Function Estimation 193 6.1 6.2 193 195 197 Overview Parametric Conditional Mean Models 6.2.1 (Re)-interpretation of Conditional Mean Models 6.2.2 Counterfactual Experiments and Conditional Mean Models 6.3 Local Constant Kernel Regression 6.3.1 Estimator Properties 6.3.2 The IMSE-Optimal Bandwidth and Kernel Function 6.3.3 Asymptotic Normality 6.3.4 Outlier-Resistant Local Constant Kernel Regression 6.3.5 Bandwidth Selection 6.3.6 A Coefficient of Determination for Nonparametric Regression 6.3.7 Local Constant Marginal Effects 6.4 Local Polynomial Kenud Regression 6.5 The Multivariate Local Polynomial Extension 6.6 Local Polynomial Kernel Regression andShrinkage 6.7 Multivariate Mixed-Data Marginal Effects 6.7.1 A Consistent Test for Predictor Relevance 6.8 Time Series Kernel Regression 6.9 Shape Constrained Kernel Regression 6.10 Practitioner’s Corner 6.10.1 Kernel Regression Is Weighted Least Squares Esti mation 6.10.2 Joint Determination of the Polynomial Degree and Bandwidth 6.10.3 A Consistent Nonparametric Test for Correct Para metric Specification 199 206 208 218 219 219 220 222 223 226 229 232 235 236 240 245 248 248 249 253
CONTENTS 6.10.4 6.10.5 6.10.6 6.10.7 6.10.8 ix Shape Constrained Kernel Regression A Multivariate Application of Local Linear Regression Confidence Bands and Nonparametric Estimation Assessing Model Performance Fixed-Effects Panel Data Models Problem Set 7 Conditional Mean Function Estimation with Endoge nous Predictors 7.1 Overview 7.2 Ill-Posed Inverse Problems and Identification 7.2.1 Kernel Smoothing and Ill-Posedness 7.2.2 Singular Design Matrices and Ill-Posedness 7.3 Parametric Instrumental Regression 7.4 Nonparametric Instrumental Regression 7.5 Practitioner’s Corner 7.5.1 Estimation of Engel Curves 7.5.2 Nonparametric Instrumental Regression with a Lin ear DGP 257 260 263 264 269 273 275 275 276 277 279 280 281 285 285 285 Problem Set 289 8 291 291 291 294 296 297 298 300 300 300 30! Semiparametric Conditional Mean Function Estimation 8.1 Overview 8.2 Robinson’s Partially Linear Model 8.3 Varying Coefficient Models 8.4 Semiparametric Single Index Models 8.4.1 Ichimura’s Method (Continuous Y) 8.4.2 Klein and Spady’s Method (Binary Y) 8.5 Summary 8.6 Practitioner’s Corner 8.6.1 A Specification Test for the Partially Linear Model 8.6.2 Assessing Model Performance ֊ Continuous Y Problem Set 307 9 309 309 309 311 311 Conditional Variance Function Estimation 9.1 Overview 9.2 Local Linear Conditional Variance Function Estimation 9.3 Practitioner’s Comer 9.3.1 A Simulated Illustration Problem Set 313
CONTENTS x III Appendices 315 A Large and Small Orders of Magnitude and Probability A.l Big and Small О Notation A.2 Big and Small О in Probability Notation 317 317 319 B R, RStudio, TeX, and Git B.l Installation of R and RStudio Desktop B.2 What Is R? B.2.1 R in the News B.2.2 Introduction to R B.2.3 Econometrics in R B.3 What Is RStudio Desktop? B.3.1 Introduction to RStudio B.4 Installation of TeX B.5 Installation of Git 323 323 323 324 324 324 325 325 325 325 C Computational Considerations C.l Binning Methods C.2 Transforms C.3 Parallelism C.4 Multipole and Tree-Based Methods C.5 Computationally Efficient Kernel Estimation in R 327 327 328 328 328 328 D R Markdown for Assignments D.l Source Code (R Markdown) for This Document D.2 R, RStudio, TeX, and Git D.3 What Is R Markdown? D.4 Creating a New R. Markdown Document in RStudio D.5 Including R Results in Your R Markdown Document D.6 Reading Data from a URL D.7 Including Plots D.8 Including Bulleted and Numbered Lists D.9 Including Tables D.10 Including Verbatim (i.e., Freeform) Text D.ll Typesetting Mathematics D.12 Flexible Document Creation D.13 Knitting Your R Markdown Document D.14 Printing Your Document DY5 Troubleshooting and Tips 333 333 333 333 334 334 334 335 336 337 337 337 338 338 338 339 E Practicum E.l Overview E.2 Getting Started with R 343 343 343
CONTENTS E.2.1 Reading Datasets Created by Other Software Programs E.2.2 Nonparmnetric Estimation of Density Functions E.3 Introduction to the R. Package np: Working with npudens() E.3.1 Introduction to the npksumO Function E.3.2 Applied Nonparametric Density Estimation E.3.3 Introduction to Applied Nonparametric Regression E.3.4 Advanced Use of the npksumO Function E.3.5 Consistent Nonparametric Inference E.3.6 Non-nested Model Comparison E.3.7 Semiparamctric Models E.3.8 Nonparametric Discrete Choice Models E.3.9 Shape Constrained Nonparametric Regression xi 1144 345 346 348 349 351 352 354 357 359 360 362 Bibliography 367 Author Index 391 Subject Index 397
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| indexdate | 2024-07-10T08:34:23Z |
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| isbn | 9781108483407 1108483402 |
| language | English |
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| physical | xxvi, 408 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
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| publisher | Cambridge University Press |
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| spelling | Racine, Jeffrey 1962- Verfasser (DE-588)133144720 aut An introduction to the advanced theory of nonparametric econometrics a replicable approach using R Jeffrey S. Racine Cambridge Cambridge University Press 2019 xxvi, 408 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Ökonometrie (DE-588)4132280-0 gnd rswk-swf Theorie (DE-588)4059787-8 gnd rswk-swf Nichtparametrisches Verfahren (DE-588)4339273-8 gnd rswk-swf Nichtparametrisches Verfahren (DE-588)4339273-8 s Theorie (DE-588)4059787-8 s Ökonometrie (DE-588)4132280-0 s b DE-604 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031449915&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Racine, Jeffrey 1962- An introduction to the advanced theory of nonparametric econometrics a replicable approach using R Ökonometrie (DE-588)4132280-0 gnd Theorie (DE-588)4059787-8 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4059787-8 (DE-588)4339273-8 |
| title | An introduction to the advanced theory of nonparametric econometrics a replicable approach using R |
| title_auth | An introduction to the advanced theory of nonparametric econometrics a replicable approach using R |
| title_exact_search | An introduction to the advanced theory of nonparametric econometrics a replicable approach using R |
| title_full | An introduction to the advanced theory of nonparametric econometrics a replicable approach using R Jeffrey S. Racine |
| title_fullStr | An introduction to the advanced theory of nonparametric econometrics a replicable approach using R Jeffrey S. Racine |
| title_full_unstemmed | An introduction to the advanced theory of nonparametric econometrics a replicable approach using R Jeffrey S. Racine |
| title_short | An introduction to the advanced theory of nonparametric econometrics |
| title_sort | an introduction to the advanced theory of nonparametric econometrics a replicable approach using r |
| title_sub | a replicable approach using R |
| topic | Ökonometrie (DE-588)4132280-0 gnd Theorie (DE-588)4059787-8 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd |
| topic_facet | Ökonometrie Theorie Nichtparametrisches Verfahren |
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