Conceptual econometrics using R:
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Elsevier, North Holland
[2019]
|
| Schriftenreihe: | Handbook of statistics
volume 41 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XV, 314 Seiten Ilustrationen, Diagramme 24 cm |
| ISBN: | 9780444643117 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents Contributors Preface xi xiii Part I Statistical Inference 1. Finite-sample inference and nonstandard asymptotics with Monte Carlo tests and R 3 Jean-Marie Dufour and Julien Neves 2. 1 2 3 4 5 6 7 Introduction Monte Carlo tests with continuous and discrete test statistics Pivotal Monte Carlo tests in R Example: Two-sample goodness-of-fit test Maximized Monte Carlo tests Asymptotic MMC tests MMC tests in R 7.1 Global Optimization 7.2 Optimal Choice 8 MMC tests: Examples 8.1 Behrens-Fisher problem 8.2 Unit root tests in autoregressive models 9 Conclusion Acknowledgments References 3 5 8 10 12 14 16 18 20 22 22 24 27 28 28 New exogeneity tests and causal paths 33 Hrishikesh D. Vlnod 1 2 3 Introduction 1.1 Computational agenda and decision rules Kernel regression review 2.1 Counterfactuals in kernel regressions 2.2 Kernel regression and consistency Cowles commission SEMs 3.1 Need for alternative exogeneity tests 33 38 38 40 40 41 42 v
Contents VI 4 3. Stochastic kernel causality by three criteria 4.1 First criterion Cr1 for X¡-*X¡ 4.2 Second criterion Cr2 for X¡-*Xj 4.3 Third criterion 03 for X¡^-X¡ 5 Numerical evaluation of Cr1 and Cr2 6 Stochastic dominance of four Orders 6.1 Weighted sum of signs of Cu(sd1) to Cu(sd4) 6.2 Unanimity index summarizing signs 7 Review of decision rule computations 8 Simulation for checking decision rules 9 A bootstrap exogeneity test 9.1 Summarizing sampling distributionof ui 10 Application example 10.1 Variables affecting term spread 10.2 Bootstrap inference on Estimated Causality Paths 11 Summary and final remarks Acknowledgments Appendices Appendix A. Review of graph theory Appendix B. For R code References 44 45 45 45 46 47 47 48 49 50 52 52 53 55 55 58 59 60 60 61 63 Adjusting for bias in long horizon regressions using R 65 Kenneth D. West and Zifeng Zhao 1 2 3 4. Introduction Long horizon regressions Bias adjustment for long horizon regressions 3.1 Introduction 3.2 R function longhorl 3.3 R function longhor 3.4 R functions proc_vb_maO and proc_vb_maq 4 R code for an empirical application Acknowledgment References 65 66 68 68 70 73 73 76 79 79 Hypothesis testing, specification testing, and model selection based onthe MCMCoutputusing R 81 Yong Li, Jun Yu, and Tao Zeng 1 2 3 4 5 Introduction MCMC and its implementation in R Hypothesis testing based on the MCMC output 3.1 Hypothesis testing under decision theory 3.2 The choice of loss function for hypothesis testing Specification testing based on the MCMC output Model selection based on the MCMC output 5.1 DIC for
regular models 5.2 Bayesian predictivedistribution as thelossfunction 82 83 86 86 86 94 98 98 99
Contents 5.3 Integrated DIC for latent variable models 5.4 Computing IDIC for latent variable models 6 Empirical illustrations 6.1 Statistical inference in assetpricing models 6.2 Statistical inference in stochastic volatilitymodels 7 Concluding remarks References Further reading vH 100 101 103 104 109 113 113 115 Part II Multivariate Models 5. Dynamic panel GMM using R 119 Peter C.B. Phillips and Chirok Han 1 Introduction 2 A dynamic panel model with macro drivers 3 R code for dynamic panel estimation 3.1 Data generation 3.2 Within-group estimation 3.3 Difference GMM 3.4 System GMM 3.5 Code verification and comparison 4 Simulation results 5 Conclusion References Further reading 6. Vector autoregressive movingaverage models 119 122 124 124 126 127 133 136 137 143 143 144 145 Wolfgang Scherrer and Manfred Deistler 1 2 3 4 5 6 7 ւոէրօժսժւօո Vector autoregressive moving average models Identifiability of VARMA systems State space models Identifiability of state space models Maximum likelihood estimation Initial estimates 7.1 Estimation of VARMA models—The Hannan, Rissanen, Kavalieris procedure 7.2 Estimation of state space models—The CCA subspace method 8 Model selection 9 Discussion and notes 9.1 Summary Acknowledgement References 145 147 156 163 166 170 172 172 174 176 189 189 190 190
Contents viii 7. Multivariate GARCH modelsforlarge-scale applications: A survey 193 Kris Boudt, Alexios Galanos, Scott Payseur, and Eric Zivot 1 2 3 4 5 6 7 Introduction Multivariate generalization of GARCH models Multivariate distributions 193 195 199 3.1 3.2 3.3 3.4 3.5 200 201 202 203 204 Multivariate Normal Multivariate Student Multivariate Laplace Multivariate Generalized Hyperbolic distribution Copula distributions Generalized Orthogonal GARCH models Conditional correlation GARCH models BIP and GAS MGARCH models MGARCH models using high-frequencyreturns 207 214 221 227 7.1 7.2 7.3 7.4 229 229 230 230 Realized BEKK HEAVY Realized DCC Other approaches 8 Illustration 9 Conclusion References 231 236 236 Part III Miscellaneous Topics 8. Modeling fractional responsesusing R 245 Joaquim Jose Santos Ramalho 1 2 3 4 Introduction The base case: Cross-sectional data and nounobserved heterogeneity 245 2.1 2.2 2.3 2.4 247 248 251 254 Conditional mean models Two-part models Partial effects Specification tests 247 Linearized- and exponential-fractionalestimators 258 3.1 3.2 3.3 3.4 259 260 263 268 Framework Neglected heterogeneity Endogenous regressors Smearing estimation of partial effects Panel data estimators 270 4.1 4.2 4.3 270 271 4.4 Framework Pooled random and fixed effects estimators Fixed effects estimators based on quasi- and mean-differences Correlated random effects estimators 274 276
Contents 5 Future developments Acknowledgments References 9. Quantitative game theory applied to economic problems ¡X 278 278 278 281 Sebastián Cano-Berlanga, ]osé-Manuel Giménez-Gómez, and Cori Vilella 1 2 Introduction Cooperative game theory 2.1 The core 2.2 The Shapley value 2.3 The nucleolus 2.4 Voting power 3 Marketing and game theory 3.1 The classic consumer theory 3.2 Attribution models 4 Claims problems 4.1 Claims rules 4.2 Obtaining fishing quotas 5 Concluding remarks Acknowledgments References Index 281 282 283 284 288 292 294 295 296 300 301 302 305 305 305 309
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| isbn | 9780444643117 |
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| spelling | Conceptual econometrics using R edited by Hrishikesh D. Vinod, C.R. Rao Amsterdam Elsevier, North Holland [2019] © 2019 XV, 314 Seiten Ilustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics volume 41 R Programm (DE-588)4705956-4 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Ökonometrie (DE-588)4132280-0 s R Programm (DE-588)4705956-4 s DE-604 Vinod, Hrishikesh D. 1939- (DE-588)135564700 edt Rao, Calyampudi Radhakrishna 1920-2023 (DE-588)119285924 edt Handbook of statistics volume 41 (DE-604)BV000002510 41 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031534681&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Conceptual econometrics using R Handbook of statistics R Programm (DE-588)4705956-4 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4132280-0 (DE-588)4143413-4 |
| title | Conceptual econometrics using R |
| title_auth | Conceptual econometrics using R |
| title_exact_search | Conceptual econometrics using R |
| title_full | Conceptual econometrics using R edited by Hrishikesh D. Vinod, C.R. Rao |
| title_fullStr | Conceptual econometrics using R edited by Hrishikesh D. Vinod, C.R. Rao |
| title_full_unstemmed | Conceptual econometrics using R edited by Hrishikesh D. Vinod, C.R. Rao |
| title_short | Conceptual econometrics using R |
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| topic_facet | R Programm Ökonometrie Aufsatzsammlung |
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