Panel data econometrics: Theory
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| Format: | Book |
| Language: | English |
| Published: |
London
Academic Press, an imprint of Elsevier
[2019]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxvii, 404 Seiten |
| ISBN: | 9780128143674 |
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MARC
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Contents Contributors Foreword General Introduction xv xvii xix 1. A Synopsis of Econometrics 1 Stephen C. Hall 1 2 3 4 5 6 7 8 9 introduction Some Basic Concepts Two Basic Approaches to Estimation Maximum Likelihood (ML) Generalized Method of Moments (GMM) Some Examples of Moment Conditions The Standard Linear Model and LeastSquares Failure of £(uu') = ff2/ The Vector xf is Stochastic 10 Failure of (¿^)=0 í— 00 v 1 2 5 5 10 12 13 14 14 14 7 ’ 11 Failure of , hm (#)=Ω 15 f— 00՝ ' ' 12 Cointegration 13 Conclusion Appendix Order of Magnitude and Convergence References 2. Testing and Correcting for Endogeneity in Nonlinear Unobserved Efferts Models 16 17 18 19 21 Wei Lin and Jeffrey M. Wooldridge 1 2 3 4 5 Introduction Models Linear in Parameters Exponential Model 21 23 28 3.1 3.2 3.3 28 30 31 An FE Poisson/CF Approach Estimating Average Partial Effects A CRE/Control Function Approach Probit Response Function Other Nonlinear Models 32 34 vii
viii Contents 3. 5.1 Pooled Methods 5.2 Joint Estimation Methods 6 Empirical Example 7 Extensions and Future Directions Appendix A.1 Relationship Between the FE and Mundlak Residuals A.2 Equivalence in Using the FE and Mundlak Residuals in FE Poisson Estimation References Further Reading 34 35 36 38 39 Nonlinear and Related Panel Data Models 45 39 40 42 43 William Greene and Qiushi Zhang 1 2 Introduction Nonlinear Models 2.1 Coefficients and Partial Effects 2.2 Interaction Effects 2.3 Identification Through Functional Form 2.4 Endogeneity 3 Panel Data Models 3.1 Objects of Estimation 3.2 General Frameworks 3.3 Dynamic Models 4 Nonlinear Panel Data Modeling 4.1 Fixed Effects 4.2 Random Effects Estimation and Correlated Random Effects 4.3 Robust Estimation and Inference 4.4 Attrition 4.5 Specification Tests 5 Panel Data 6 Modeling Frameworksand Applications 6.1 Binary Choice 6.2 Bivariate and Recursive Binary Choice 6.3 Ordered Choice 6.4 Censored or Truncated Regression 6.5 Stochastic Frontier: Panel Models 6.6 Count Data 6.7 A General Nonlinear Regression 6.8 Sample Selection Models 6.9 Individual Choice and Stated Choice Experiments 6.10 Multilevel Models Hierarchical (Nonlinear) Models 6.11 Fixed Effects With Large N and Large T References 45 48 50 51 51 52 53 54 58 60 61 61 68 71 73 74 76 78 78 84 85 85 85 86 88 88 89 90 90 91
Contents 4. Nonparametric Estimation and Inference for Panel Data Models ix 97 Christopher F. Parmeter and Jeffrey S. Racine 1 2 3 introduction How Unobserved Heterogeneity Complicates Estimation Estimation in the Random Effects Framework 3.1 3.2 3.3 3.4 4 5 6 5. Preliminaries Local-Polynomial Weighted Least-Squares Spline-Based Estimation Profile Likelihood Estimation 97 98 99 99 100 103 106 Estimation in the Fixed Effects Framework 108 4.1 4.2 4.3 4.4 108 110 112 113 DifferencingДransformation Methods Profile Estimation Marginal Integration Profile Least Squares Dynamic Panel Estimation 116 5.1 11 7 The Static Setting Inference 118 6.1 6.2 6.3 6.4 118 122 124 125 Poolability Specification Testing ' A Hausman Test Simultaneous Confidence Bounds 7 Conclusions Acknowledgments References 127 127 127 Heterogeneity and Endogeneity in Panel Stochastic Frontier Models 131 Levent Kutlu and Kien C. Tran 1 2 3 4 Introduction Panel Stochastic Frontier Models WithHeterogeneity Panel Stochastic Frontier Models WithEndogeneity Panel Stochastic Frontier Models With Both Heterogeneity and Endogeneity 5 Concluding Remarks References 6. Bayesian Estimation of Panel Count Data Models: Dynamics, Latent Heterogeneity, Serial Error Correlation, and Nonparametric Structures 131 132 138 141 143 143 147 Stefanos Dimitrakopoulos 1 2 Introduction Bayesian Preliminaries 2.1 Bayesian Statistics 147 150 150
x Contents 2.2 Markov Chain MonteCarlo Simulation Methods 2.3 Bayesian Nonparametric Models 3 Parametric Panel Count DataRegression Models 3.1 The Static Poisson Model 3.2 Extension I: The Dynamic Poisson Model 3.3 Extension II: The Dynamic Poisson Model With Latent Heterogeneity 3.4 Extension III: The Dynamic Poisson Model With Latent Heterogeneity and Serial Error Correlation 4 Semiparametric Panel CountData Regression Models 5 Prior-Posterior Analysis 5.1 The Models of Interest 5.2 Prior Specification 5.3 Posterior Sampling 5.4 Model Comparison 6 Conclusions References Further Reading 7. Fixed Effects Likelihood Approachfor Large Panels 150 152 157 157 157 158 159 160 161 161 162 162 170 171 171 173 175 Chihwa Kao and Fa Wang 8. 1 2 3 Introduction Notations and Preliminaries Fixed Dimensional Case 3.1 Consistency 3.2 Asymptotic Expansion 3.3 Bias Calculation 4 Panel With Individual Efferts 4.1 Consistency 4.2 Asymptotic Expansion 4.3 Bias Calculation 5 Panel With individua! Effects and Time Effects 5.1 Consistency 5.2 Asymptotic Expansion 5.3 Bias Calculation 6 Conclusions Acknowledgment References 175 177 179 179 180 181 181 181 182 188 188 189 189 193 195 195 196 Panel Vector Autoregressions WithBinary Data 197 Bo E. Honoré and Ekaterini Kyriazidou 1 2 Introduction The Univariate Logit Model 2.1 Static Case 197 199 199
Contents x¡ 2.2 2.3 3 4 5 6 9. Dynamic Case (Pure AR(D) Dynamic Case WithExogenous Covariates 202 204 The Bivariate Pure VAR(1) Logit Case The Bivariate Logit Mode! With Exogenous Covariates The General М-Variate, General TVAR(1) Case Contemporaneous Cross-Equation Dependence 206 209 212 213 6.1 6.2 213 216 Static Case Dynamic Case 7 Monte CarloExperiments 8 Conclusions Acknowledgments References 219 220 221 221 Implementing Generalized Panel Data Stochastic Frontier Estimators 225 Subal C. Kumbhakar and Christopher F. Parmeter 1 2 3 4 Introduction Earlier Models and Shortcomings The Generalized Panel Data Stochastic Frontier Model 225 226 230 3.1 3.2 3.3 232 236 239 240 242 Plug-in Estimation Full MaximumLikelihood Maximum Simulated Likelihood Including Determinants of Inefficiency 4.1 Semiparametric Approaches 5 10. Recent Applications of the Generalized Pane! Data Stochastic Frontier Model 6 Conclusion Acknowledgment References 244 246 246 246 Panel Cointegration Techniques and Open Challenges 251 Peter Pedroni 1 2 3 4 5 6 7 Introduction Cointegration and the Motivation for Panels Strategies for Treating Cross-sectional Heterogeneity in Cointegration Testing and Inference Treating Heterogeneity in Residual Based Tests for Cointegration Comparison of Residual Based and Error Correction Based Testing Estimation and Testing of Cointegrating Relationships in Heterogeneous Panels Testing Directions of Long-Run Causality in Heterogeneous Cointegrated Panels 251 254 258 259 263 266 270
xii Contents 8 Strategies for Treating Cross-Sectional Dependence in Heterogeneous Panels 9 A Nonparametric Rank Based Approach to Some Open Challenges 10 New Directions and Challenges for Nonlinear and Time Varying Long-Run Relationships References 11. Alternative Approaches to the Econometrics of Panel Data 274 277 282 286 289 P.A.V.B. Swamy, Peter von zur Mueblen, Jatinder S. Mehta and І-Lok Chang 1 2 3 Introduction Models With Nonunique Coefficients and Error Terms for Panel Data Models With Unique Coefficients and Error Terms for Panel Data 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 4 Bayesian Analysis of Panel Data 4.1 292 296 296 297 298 299 302 304 308 316 323 Improvements in the Precisions of the Estimators of Time-Invariant and Individual-Specific Coefficients of the Stochastic Law Simulation-Based Estimation and Inference 327 329 Empirical Evidence of the Causal Effects of Wives' Education on Their Earnings 330 4.2 5 Linear-in-Variables and Nonlinear-in-Coefficients Functional Form for Economic Relationships A Deterministic Law Derivation of the Unique Error Term From the Deterministic Law Stochastic Law With Unique Coefficients andError Term Stochastic Law in Terms of Observable Variables Simultaneous Estimation of the Bias-Free and Omitted-Regressor Bias Components of the Coefficient on Each Nonconstant Regressor of aStochastic Law Assumptions Appropriate to Time-Series Data Sets Within a Given Panel Data Set Assumptions Appropriate to Cross-Sectional Data Sets Within a Given Panel Data Set 290 5.1 Illuminating the Contrast Between (72) and (79) and an Estimated Earnings
and Education Relationship Based on an Incorrectly Specified Error Term in (4) 6 Conclusions Appendix A Proof of the Uniqueness of the Coefficients and Error Term of a Stochastic Law Appendix В Conditions for the Consistency of Certain Estimators of the Coefficients of aStochastic Law References Further Reading 338 339 340 341 341 344
Contents 12. Analysis of Panel Data Using R xiii 345 Arne Henningsen and Géraldine Henningsen 1 2 3 4 Introduction Loading Data Exploring the Data Sets OLS Regression 4.1 Pooled OLS Regression 4.2 Least-Squares Dummy Variables Estimation 5 Organization, Management, and Preparation of Panel Data 5.1 Re-Ordering of Observations 5.2 Conversion between "Long Format" and "Wide Format" 5.3 Creating a Balanced Panel Data Set from an Unbalanced Panel Data Set 5.4 Aggregating Panel Data Sets 5.5 Preparing the Data Set for Estimations with the plm Package 5.6 Lagged Variables and First Differences 6 Estimation of OLS-Based Linear Panel Data Models 6.1 Variable Coefficients Model 6.2 Fixed-Effects Estimator based on "Within" Transformation 6.3 Pooled Estimation 6.4 Testing Poolability 6.5 Obtaining Estimates of the Fixed Effects 6.6 First-Difference Estimator 7 "Between" Estimator for Panel Data 7.1 Specification of the "Between" Estimator 7.2 Estimation of the "Between" Estimator 8 Linear Random-Effects Panel Data Models 8.1 Specification of the Random-Effects Model 8.2 Estimation of the Random-Effects Model 8.3 Estimates of the Variance of the Error Components 8.4 Testing the Assumptions of the Random-Effects Model 9 Tests for the Error Structure in Panel Data Models 9.1 Tests for Serial Correlation 9.2 Tests for Cross-Sectional Dependence 10 How to Handle Serial Correlation 10.1 Parameter Tests with Robust Covariance Matrices 10.2 FGLS Estimator 11 Simple Instrumental Variable Estimators 11.1 Fixed-Effects Estimation 11.2 Random-Effects Estimation 11.3 Hausman Test 12 Panel Time
Series Models 13 Unit Root Tests for Panel Data 14 Dynamic Panel Data Models 14.1 Tests for Panel GMM 346 347 348 350 350 351 354 354 354 356 357 357 359 360 360 361 363 364 369 371 373 373 373 374 374 375 375 375 377 377 381 382 382 384 385 385 386 386 387 388 389 392
xiv Contents 15 Systems of Linear Equations 16 Conclusion Acknowledgments References Index 392 395 395 395 397 |
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| spelling | Panel data econometrics Theory edited by Mike Tsionas London Academic Press, an imprint of Elsevier [2019] xxvii, 404 Seiten txt rdacontent n rdamedia nc rdacarrier Panelanalyse (DE-588)4173172-4 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 s Panelanalyse (DE-588)4173172-4 s DE-604 Tsionas, Efthymios G. 1965-2024 (DE-588)171335589 edt (DE-604)BV046080661 1 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=031461859&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Panel data econometrics |
| title_auth | Panel data econometrics |
| title_exact_search | Panel data econometrics |
| title_full | Panel data econometrics Theory edited by Mike Tsionas |
| title_fullStr | Panel data econometrics Theory edited by Mike Tsionas |
| title_full_unstemmed | Panel data econometrics Theory edited by Mike Tsionas |
| title_short | Panel data econometrics |
| title_sort | panel data econometrics theory |
| topic | Panelanalyse (DE-588)4173172-4 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Panelanalyse Ökonometrie |
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