Applied nonparametric econometrics:
"Bridging the gap between applied economists and theoretical nonparametric econometricians, this book explains basic to advanced nonparametric methods with applications"..
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2015
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Summary: | "Bridging the gap between applied economists and theoretical nonparametric econometricians, this book explains basic to advanced nonparametric methods with applications".. |
| Physical Description: | XII, 367 S. graph. Darst. 26 cm |
| ISBN: | 9781107010253 9780521279680 |
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Record in the Search Index
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| adam_text | Titel: Applied nonparametric econometrics
Autor: Henderson, Daniel J
Jahr: 2015
Contents Introduction page 1 1.1 Overview 1 1.2 Birth of the text 2 1.3 Who will benefit 3 1.4 Why this book is relevant 3 1.5 Examples 4 1.5.1 CO 2 emissions 4 1.5.2 Age earnings 5 1.5.3 Hedonic price function 6 1.6 Examples in the text 7 1.6.1 Density 8 1.6.2 Regression 9 1.7 Outline of the remainder of the book 9 1.8 Supplemental materials 11 1.9 Acknowledgments 12 Univariate density estimation 15 2.1 Smoothing preliminaries 16 2.2 Estimation 19 2.2.1 A crude estimator 19 2.2.2 Naive estimator 22 2.2.3 Kernel estimator 24 2.3 Kernel selection 28 2.4 Kernel efficiency 29 2.5 Bandwidth selection 30 2.5.1 Optimal selection 30 2.5.2 Data-driven methods 33 2.5.3 Plug-in or cross-validation? 43 2.6 Density derivatives 45 2.6.1 Bias and variance 47 2.6.2 Bandwidth selection 48 2.6.3 Relative efficiency 50 2.7 Application 50 2.7.1 Histograms 51 2.7.2 Kernel densities 52 vu
59 59 62 64 68 70 70 72 73 74 75 76 83 84 86 87 89 89 91 92 97 99 101 102 105 106 106 108 108 109 110 111 112 113 114 117 117 118 118 120 121 121 123 124 Contents Multivariate density estimation 3.1 Joint densities 3.2 Bias, variance, and AMISE 3.3 The curse of dimensionality 3.4 Bandwidth selection 3.4.1 Rule-of-thumb bandwidth selection 3.4.2 Cross-validation bandwidth selection 3.5 Conditional density estimation 3.5.1 Bias, variance, and AMSE 3.5.2 Bandwidth selection 3.5.3 Inclusion of irrelevant variables 3.6 Application Inference about the density 4.1 Fundamentals 4.1.1 Consistent test 4.1.2 Distance measures 4.1.3 Centering terms 4.1.4 Degenerate U-statistics 4.1.5 Bootstrap 4.2 Equality 4.3 Parametric specification 4.4 Independence 4.5 Symmetry 4.6 Silverman test for multimodality 4.7 Testing in practice 4.7.1 Bootstrap versus asymptotic distribution 4.7.2 Role of bandwidth selection on reliability of tests 4.8 Application 4.8.1 Equality 4.8.2 Correct parametric specification 4.8.3 Independence 4.8.4 Symmetry 4.8.5 Modality Regression 5.1 Smoothing preliminaries 5.2 Local-constant estimator 5.2.1 Derivation from density estimators 5.2.2 An indicator approach 5.2.3 Kernel regression on a constant 5.3 Bias, variance, and AMISE of the LCLS estimator 5.4 Bandwidth selection 5.4.1 Univariate digression 5.4.2 Optimal bandwidths in higher dimensions 5.4.3 Least-squares cross-validation
Contents IX 5.4.4 Cross-validation based on Akaike information criteria 125 5.4.5 Interpretation of bandwidths for LCLS 126 5.5 Gradient estimation 127 5.6 Limitations of LCLS 128 5.7 Local-linear estimation 130 5.7.1 Choosing LLLS over LCLS 131 5.7.2 Efficiency of the local-linear estimator 132 5.8 Local-polynomial estimation 133 5.9 Gradient-based bandwidth selection 135 5.10 Standard errors and confidence bounds 137 5.10.1 Pairs bootstrap 137 5.10.2 Residual bootstrap 138 5.10.3 Wild bootstrap 139 5.11 Displaying estimates 139 5.12 Assessing fit 141 5.13 Prediction 141 5.14 Application 142 5.14.1 Data 143 5.14.2 Results 144 6 Testing in regression 159 6.1 Testing preliminaries 160 6.1.1 Goodness-of-fit tests 160 6.1.2 Conditional-moment test 161 6.2 Correct parametric specification 162 6.2.1 Goodness-of-fit test 163 6.2.2 Conditional-moment test 166 6.3 Irrelevant regressors 168 6.3.1 Goodness-of-fit test 168 6.3.2 Conditional-moment test 169 6.4 Heteroskedasticity 171 6.5 Testing in practice 174 6.5.1 Bootstrap versus asymptotic distribution 174 6.5.2 Role of bandwidth selection on reliability of tests 175 6.6 Application 177 6.6.1 Correct functional form 177 6.6.2 Relevance 180 6.6.3 Heteroskedasticity 180 6.6.4 Density tests 182 7 Smoothing discrete variables 187 7.1 Estimation of a density 188 7.1.1 Kernels for smoothing discrete variables 188 7.1.2 Generalized product kernel 190 7.2 Finite sample properties 191 7.2.1 Discrete-only bias 191
X Contents 7.2.2 Discrete-only variance 192 7.2.3 Discrete-only MSE 192 7.2.4 Mixed-data bias 193 7.2.5 Mixed-data variance 193 7.2.6 Mixed-data MSE 193 7.3 Bandwidth estimation 194 7.3.1 Discrete-data only 195 7.3.2 Mixed data 196 7.4 Why the faster rate of convergence? 197 7.5 Alternative discrete kernels 198 7.6 Testing 199 7.7 Application 201 8 Regression with discrete covariates 205 8.1 Estimation of the conditional mean 206 8.1.1 Local-constant least-squares 206 8.1.2 Local-linear least-squares 208 8.2 Estimation of gradients 209 8.2.1 Continuous covariates 209 8.2.2 Discrete covariates 210 8.3 Bandwidth selection 212 8.3.1 Automatic bandwidth selection 213 8.3.2 Upper and lower bounds for discrete bandwidths 214 8.4 Testing 215 8.4.1 Correct parametric specification 215 8.4.2 Significance of continuous regressors 216 8.4.3 Significance of discrete regressors 217 8.5 All discrete regressors 220 8.6 Application 222 8.6.1 Bandwidths 222 8.6.2 Elasticities 223 8.6.3 Numerical gradients 223 8.6.4 Testing 225 9 Semiparametric methods 227 9.1 Semiparametric efficiency 228 9.2 Partially linear models 228 9.2.1 Estimation 229 9.2.2 Bandwidth selection 232 9.2.3 Testing 233 9.3 Single-index models 238 9.3.1 Estimation 239 9.3.2 Bandwidth selection 244 9.3.3 Testing 245 9.4 Semiparametric smooth coefficient models 247 9.4.1 Estimation 249
Contents xi 9.4.2 Bandwidth selection 252 9.4.3 Testing 252 9.5 Additive models 254 9.5.1 Estimation 255 9.5.2 Bandwidth selection 258 9.5.3 Testing 259 9.6 Application 261 9.6.1 Bandwidths 261 9.6.2 Plotting estimates 263 9.6.3 Specification testing 264 10 Instrumental variables 267 10.1 The ill-posed inverse problem 268 10.2 Tackling the ill-posed inverse 270 10.3 Local-polynomial estimation of the control-function model 272 10.3.1 Multiple endogenous regressors 274 10.3.2 Bandwidth selection 275 10.3.3 Choice of polynomial order 276 10.3.4 Simulated evidence of the counterfactual simplification 278 10.3.5 A valid bootstrap procedure 279 10.4 Weak instruments 280 10.4.1 Weak identification 282 10.4.2 Estimation in the presence of weak instruments 284 10.4.3 Importance of nonlinearity in the first stage 286 10.5 Discrete endogenous regressor 286 10.6 Testing 287 10.7 Application 288 11 Panel data 293 11.1 Pooled models 294 11.2 Random effects 295 11.2.1 Local-linear weighted least-squares 297 11.2.2 Wang’s iterative estimator 298 11.3 Fixed effects 301 11.3.1 Additive individual effects 302 11.3.2 Discrete individual effects 305 11.4 Dynamic panel estimation 306 11.5 Semiparametric estimators 308 11.6 Bandwidth selection 309 11.7 Standard errors 309 11.7.1 Pairs bootstrap 310 11.7.2 Residual bootstrap 310 11.8 Testing 311 11.8.1 Poolability 311 11.8.2 Functional form specification 313 11.8.3 Nonparametric Hausman test 315
Contents xii 11.9 Application 316 11.9.1 Bandwidths 317 11.9.2 Estimation 318 11.9.3 Testing 318 12 Constrained estimation and inference 321 12.1 Rearrangement 322 12.1.1 Imposing convexity 324 12.1.2 Existing literature 325 12.2 Motivating alternative shape-constrained estimators 326 12.3 Implementation methods via reweighting 330 12.3.1 Constraint-weighted bootstrapping 330 12.3.2 Data sharpening 330 12.4 Practical issues 331 12.4.1 Selecting the distance metric 331 12.4.2 Choice of smoothing parameter 332 12.4.3 Linear in p implementation issues 333 12.4.4 Imposing additive separability 336 12.5 Hypothesis testing on shape constraints 337 12.6 Further extensions 338 12.7 Application 339 12.7.1 Imposing positive marginal product 339 12.7.2 Imposing constant returns to scale 340 Bibliography 343 Index 359
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| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
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| indexdate | 2024-07-10T01:23:02Z |
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| isbn | 9781107010253 9780521279680 |
| language | English |
| lccn | 014005138 |
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| physical | XII, 367 S. graph. Darst. 26 cm |
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| spelling | Henderson, Daniel J. Verfasser (DE-588)129907421 aut Applied nonparametric econometrics Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami 1. publ. Cambridge Cambridge University Press 2015 XII, 367 S. graph. Darst. 26 cm txt rdacontent n rdamedia nc rdacarrier "Bridging the gap between applied economists and theoretical nonparametric econometricians, this book explains basic to advanced nonparametric methods with applications".. BUSINESS & ECONOMICS / Econometrics bisacsh Wirtschaft Econometrics Nonparametric statistics BUSINESS & ECONOMICS / Econometrics Nichtparametrisches Verfahren (DE-588)4339273-8 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Nichtparametrisches Verfahren (DE-588)4339273-8 s Ökonometrie (DE-588)4132280-0 s DE-604 Parmeter, Christopher F. Verfasser (DE-588)137578490 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027917888&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Henderson, Daniel J. Parmeter, Christopher F. Applied nonparametric econometrics BUSINESS & ECONOMICS / Econometrics bisacsh Wirtschaft Econometrics Nonparametric statistics BUSINESS & ECONOMICS / Econometrics Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4339273-8 (DE-588)4132280-0 |
| title | Applied nonparametric econometrics |
| title_auth | Applied nonparametric econometrics |
| title_exact_search | Applied nonparametric econometrics |
| title_full | Applied nonparametric econometrics Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami |
| title_fullStr | Applied nonparametric econometrics Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami |
| title_full_unstemmed | Applied nonparametric econometrics Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami |
| title_short | Applied nonparametric econometrics |
| title_sort | applied nonparametric econometrics |
| topic | BUSINESS & ECONOMICS / Econometrics bisacsh Wirtschaft Econometrics Nonparametric statistics BUSINESS & ECONOMICS / Econometrics Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | BUSINESS & ECONOMICS / Econometrics Wirtschaft Econometrics Nonparametric statistics Nichtparametrisches Verfahren Ökonometrie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027917888&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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