Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology
The collection of chapters in Volume 43 Part B of Advances in Econometricsserves as a tribute to one of the most innovative, influential, and productive econometricians of his generation, Professor M. Hashem Pesaran
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bingley
Emerald Publishing Limited
2022
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Advances in Econometrics Series
v.43, Part B |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Zusammenfassung: | The collection of chapters in Volume 43 Part B of Advances in Econometricsserves as a tribute to one of the most innovative, influential, and productive econometricians of his generation, Professor M. Hashem Pesaran |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (372 Seiten) |
| ISBN: | 9781802620658 |
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| 505 | 8 | |a Intro -- Half Title Page -- Series editors Page -- Title Page -- Copyright Page -- Contents -- Introduction -- Part B: Panel Modeling, Micro Applications, and Econometric Methodology -- Part B1. Panel Data Methods -- Part B2. Micro Modeling -- Part B3. Econometric Methodologies -- References -- Part B1: Panel Data Methods -- Chapter 1: A Panel Data Model With Generalized Higher-Order Network Effects -- 1. Introduction -- 2. Model -- 2.1. Notation and Outline -- 2.2. Key Assumptions -- 3. Monte Carlo simulations -- 3.1. Design -- 3.2. Results -- 3.3. Likelihood-Ratio Tests -- 4. Conclusions -- References -- Chapter 2: Spatial and Spatio-Temporal Error Correction, Networks and Common Correlated Effects -- 1. Introduction -- 2. Spatial and Spatio-Temporal Error Correction Models -- 3. Spatio-Temporal ECM, Common Correlated Effects and Weak Dependence -- 4. An Application to House Prices in the UK -- 4.1. Economic Model -- 4.2. Data -- 4.3. Discussion of Results -- 5. Conclusion -- Acknowledgments -- References -- Appendix: Estimation Steps -- Chapter 3: Heterogeneity and Dynamic Dependence in Panel Analysis of Individual Behavior -- 1. Introduction -- 2. Dynamic Response Path of Labor Participation Decision to a Health Shock -- Parameter Heterogeneity (Stability) of a State-Dependent Model -- Concluding Remarks -- References -- Appendix -- A Cohort Approach to Estimate Fixed Individual-Specific Effects for a Dynamic Binary State-Dependent Model -- Chapter 4: Multiple Treatment Effects in Panel-Heterogeneity and Aggregation -- 1. Introduction -- 2. Measurement of Treatment Effects and Sample Configuration -- 3. Homogeneous or Heterogeneous Treatment Effects -- 4. Aggregation Methods -- 5. To Aggregate or Not -- 5.1. Criterion -- 5.2. Test of Significance between the Aggregate and Disaggregate Approaches | |
| 505 | 8 | |a Test 1: Diebold and Mariano (1995) Asymptotic Test -- Test 2. The Sign Test -- Test 3. The Wilcoxon's Signed-Rank Test -- 6. Monte Carlo Simulation -- 7. Analysis of China's P2P Market -- 7.1. A Brief History -- 7.2. The Model and the Data -- 8. Conclusion -- References -- Chapter 5: Backward Mean Transformation in Panel Data with Predetermined Regressors -- 1. Introduction -- 2. The Model -- 3. Asymptotic Theory -- 3.1. Stationary Setup -- 3.2. Mean Non-stationarity -- 3.3. A Stylized Autoregressive Model -- 4. Other Approaches Based on Backward Means -- 4.1. Bias-Corrected OBM -- 4.2. OLS-IV Estimator of Everaert (2013) -- 4.3. Other Hybrid OLS-IV Estimators -- 4.4. Explanatory Notes on Everaert (2013) -- 5. Finite Sample Results -- 6. Concluding Remarks -- References -- Appendix. Tables: Monte Carlo Results for the Dynamic Model -- Chapter 6: Various Asymptotic Distributions of the Error-Components Test for Cross-Sectional Correlation -- Introduction -- 2. A Review on the Double-Indexed CLT -- 3. Testing for Cross-Sectional Correlation -- 3.1. The Conventional Test Statistic -- 3.2. A Modified Test Statistic -- 3.3. Simulating the Critical Values for Fixed T, Equation (17) -- 4. A Monte Carlo Experiment -- References -- Appendix: Technical Proofs -- Chapter 7: Trimmed Mean Group Estimation -- 1. Introduction -- 2. Motivation -- 2.1. Weighted Mean Group Estimation -- 2.2. Trimming Based on the Variances of Regressors -- 2.3. Examples -- 2.4. Trimming Weights -- 3. Trimmed Mean Group Estimation -- 3.1. Trimmed MG Estimator for Two‐Way FE Models -- 3.2. Trimmed CCEMG Estimator -- 4. Monte Carlo Simulation -- 5. Empirical Illustration: Effect of Police on Crime -- 6. Concluding Remarks -- References -- Part B2: Micro Modeling -- Chapter 8: Corporate Indebtedness and Low Productivity Growth of Italian Firms -- 1. Introduction -- 2. Data | |
| 505 | 8 | |a 3. Descriptive Statistics -- 3.1. Coverage -- 3.2. Evolution of Firm Indebtedness -- 3.3. Firm Productivity -- 4. Empirical Framework -- 4.1. Auto-Regressive Distributed Lag (ARDL) Approach -- 4.1.1. Estimation and Panel Tests of Threshold Effects -- 4.2. Distributed Lag (DL) Approach -- 4.3. Cross-Sectionally Augmented ARDL and DL Approaches -- 5. Results -- 5.1. Estimates of Long-Run Effects -- 5.2. Tests of Corporate-Debt Threshold Effects -- 5.3. Economic Significance -- 6. Conclusion -- References -- Chapter 9: Women's Potential Earnings Distributions -- 1. Introduction -- 2. Empirical Methods -- 2.1. Basic Notations -- 2.2. Comparing Two Distributions: Entropy‐Based Measures and Stochastic Dominance Tests -- 2.2.1. Entropy‐Based Measures -- 2.2.2. Stochastic Dominance -- 2.3. Identification and Estimation of Counterfactual Distributions -- 2.3.1. Without Selection -- 2.3.2. Accounting for Selection -- 3. Data -- 4. Baseline Results -- 4.1. Female Wage Versus Counterfactual Distribution #1 -- 4.1.1. Entropy and Conventional Measures of the Differences -- 4.1.2. Stochastic Dominance Test Results -- 4.2. Female Wage Versus Counterfactual Distribution #2 -- 5. Results Addressing Selection -- 6. Conclusions -- References -- Part B3: Econometric Methodologies -- Chapter 10: Where (and by How Much) Does a Theory Break Down? With an Application to the Expectation Hypothesis -- 1. Introduction -- 2. The EH: Implied Relations and Previous Findings -- 3. NP Evidence: Where Do the Violations of the EH Come From? -- 4. The Last Straw: How Many Observations Cause the Rejection of the Hypothesis? -- 4.1. RLS on the Sorted Data -- 4.2. LS with Sequential Thresholds, on the Original Data -- 4.3. Further Discussion of the Methodology -- 5. Concluding Comments -- References -- Chapter 11: Gaussian Rank Correlation and Regression -- 1. Introduction | |
| 505 | 8 | |a 2. Theoretical Background -- 2.1. Econometric Model -- 2.2. The Score Vector and the Hessian Matrix -- 2.3. Partial Correlation and Regression -- 3. Asymptotic Properties under Correct Specification -- 3.1. When Margins Are Known -- 3.1.1. Information Matrix Equality -- 3.1.2. Asymptotic Distribution of the ML Estimators -- 3.2. Replacing Margins with Empirical cdf's -- 3.3. Efficiency Comparison with Other Moment Estimators -- 3.3.1. Correlation Measures -- 3.3.2. Partial Correlation Coefficients and Regression -- 4. Misspecification Analysis -- 4.1. Pseudo-true Values -- 4.2. Asymptotic Distribution -- 5. Comparison with Alternative Estimators -- 5.1. Spearman Correlation -- 5.2. Pearson Correlation -- 5.3. Comparison -- 6. Monte Carlo Evidence -- 6.1. Design and Estimation Details -- 6.2. Sampling Distribution of the Different Estimators -- 6.3. Finite Sample Inference -- 6.4. The Effect of Outliers -- 7. Empirical Applications -- 7.1. Migration and Growth Rates -- 7.2. The Augmented Solow Growth Model -- 8. Conclusions and Directions for Further Research -- References -- Appendix A.Some Practical Considerations -- Chapter 12: Robust Dynamic Panel Data Models Using ε-Contamination -- 1. Introduction -- 2. A Robust Linear Dynamic Panel Data Model -- 2.1. The Static Framework -- 2.2. The Dynamic Framework -- 2.3. The Robust Dynamic Linear Model in the Two-stage Hierarchy -- 2.3.1. The First Step of the Robust Bayesian Estimator -- 2.3.2. The Second Step of the Robust Bayesian Estimator -- 2.4. Estimating the ML-II Posterior Variance-Covariance Matrix -- 3. Monte Carlo Simulation Study -- 3.1. The DGP of the Monte Carlo Simulation Study -- 3.1.1. The random effects World -- 3.1.2. The Chamberlain-type fixed effects World -- 3.1.3. The Hausman-Taylor World -- 3.1.4. The Dynamic Homogeneous Panel Data World with Common Trends | |
| 505 | 8 | |a 3.1.5. The Dynamic Homogeneous Panel Data World with Common Correlated Effects -- 3.1.6. The Dynamic Heterogeneous Panel Data World with Common Correlated Effects -- 4. Conclusion -- Notes -- References -- Chapter 13: Identification‐robust Inference for Endogeneity Parameters in Models with an Incomplete Reduced Form -- 1. Introduction -- 2. Framework and Notation -- 2.1. Basic Structural Framework -- 2.2. Notation -- 3. Asymptotic Assumptions -- 4. Two‐stage inference for endogeneity covariances -- 4.1. Inference for Structural Parameter β -- 4.2. Conditional Point Estimation of Endogeneity Covariances -- 4.3. Joint Inference for Structural Parameters and Endogeneity Covariances -- 4.4. Projection‐based Inference for Endogeneity Covariances -- 5. Inference for Total Effect -- 6. Exogeneity Tests -- 7. Application to Return‐to‐Schooling Model -- 7.1. Data -- 7.2. Results -- 8. Conclusion -- References -- Index | |
| 520 | |a The collection of chapters in Volume 43 Part B of Advances in Econometricsserves as a tribute to one of the most innovative, influential, and productive econometricians of his generation, Professor M. Hashem Pesaran | ||
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| contents | Intro -- Half Title Page -- Series editors Page -- Title Page -- Copyright Page -- Contents -- Introduction -- Part B: Panel Modeling, Micro Applications, and Econometric Methodology -- Part B1. Panel Data Methods -- Part B2. Micro Modeling -- Part B3. Econometric Methodologies -- References -- Part B1: Panel Data Methods -- Chapter 1: A Panel Data Model With Generalized Higher-Order Network Effects -- 1. Introduction -- 2. Model -- 2.1. Notation and Outline -- 2.2. Key Assumptions -- 3. Monte Carlo simulations -- 3.1. Design -- 3.2. Results -- 3.3. Likelihood-Ratio Tests -- 4. Conclusions -- References -- Chapter 2: Spatial and Spatio-Temporal Error Correction, Networks and Common Correlated Effects -- 1. Introduction -- 2. Spatial and Spatio-Temporal Error Correction Models -- 3. Spatio-Temporal ECM, Common Correlated Effects and Weak Dependence -- 4. An Application to House Prices in the UK -- 4.1. Economic Model -- 4.2. Data -- 4.3. Discussion of Results -- 5. Conclusion -- Acknowledgments -- References -- Appendix: Estimation Steps -- Chapter 3: Heterogeneity and Dynamic Dependence in Panel Analysis of Individual Behavior -- 1. Introduction -- 2. Dynamic Response Path of Labor Participation Decision to a Health Shock -- Parameter Heterogeneity (Stability) of a State-Dependent Model -- Concluding Remarks -- References -- Appendix -- A Cohort Approach to Estimate Fixed Individual-Specific Effects for a Dynamic Binary State-Dependent Model -- Chapter 4: Multiple Treatment Effects in Panel-Heterogeneity and Aggregation -- 1. Introduction -- 2. Measurement of Treatment Effects and Sample Configuration -- 3. Homogeneous or Heterogeneous Treatment Effects -- 4. Aggregation Methods -- 5. To Aggregate or Not -- 5.1. Criterion -- 5.2. Test of Significance between the Aggregate and Disaggregate Approaches Test 1: Diebold and Mariano (1995) Asymptotic Test -- Test 2. The Sign Test -- Test 3. The Wilcoxon's Signed-Rank Test -- 6. Monte Carlo Simulation -- 7. Analysis of China's P2P Market -- 7.1. A Brief History -- 7.2. The Model and the Data -- 8. Conclusion -- References -- Chapter 5: Backward Mean Transformation in Panel Data with Predetermined Regressors -- 1. Introduction -- 2. The Model -- 3. Asymptotic Theory -- 3.1. Stationary Setup -- 3.2. Mean Non-stationarity -- 3.3. A Stylized Autoregressive Model -- 4. Other Approaches Based on Backward Means -- 4.1. Bias-Corrected OBM -- 4.2. OLS-IV Estimator of Everaert (2013) -- 4.3. Other Hybrid OLS-IV Estimators -- 4.4. Explanatory Notes on Everaert (2013) -- 5. Finite Sample Results -- 6. Concluding Remarks -- References -- Appendix. Tables: Monte Carlo Results for the Dynamic Model -- Chapter 6: Various Asymptotic Distributions of the Error-Components Test for Cross-Sectional Correlation -- Introduction -- 2. A Review on the Double-Indexed CLT -- 3. Testing for Cross-Sectional Correlation -- 3.1. The Conventional Test Statistic -- 3.2. A Modified Test Statistic -- 3.3. Simulating the Critical Values for Fixed T, Equation (17) -- 4. A Monte Carlo Experiment -- References -- Appendix: Technical Proofs -- Chapter 7: Trimmed Mean Group Estimation -- 1. Introduction -- 2. Motivation -- 2.1. Weighted Mean Group Estimation -- 2.2. Trimming Based on the Variances of Regressors -- 2.3. Examples -- 2.4. Trimming Weights -- 3. Trimmed Mean Group Estimation -- 3.1. Trimmed MG Estimator for Two‐Way FE Models -- 3.2. Trimmed CCEMG Estimator -- 4. Monte Carlo Simulation -- 5. Empirical Illustration: Effect of Police on Crime -- 6. Concluding Remarks -- References -- Part B2: Micro Modeling -- Chapter 8: Corporate Indebtedness and Low Productivity Growth of Italian Firms -- 1. Introduction -- 2. Data 3. Descriptive Statistics -- 3.1. Coverage -- 3.2. Evolution of Firm Indebtedness -- 3.3. Firm Productivity -- 4. Empirical Framework -- 4.1. Auto-Regressive Distributed Lag (ARDL) Approach -- 4.1.1. Estimation and Panel Tests of Threshold Effects -- 4.2. Distributed Lag (DL) Approach -- 4.3. Cross-Sectionally Augmented ARDL and DL Approaches -- 5. Results -- 5.1. Estimates of Long-Run Effects -- 5.2. Tests of Corporate-Debt Threshold Effects -- 5.3. Economic Significance -- 6. Conclusion -- References -- Chapter 9: Women's Potential Earnings Distributions -- 1. Introduction -- 2. Empirical Methods -- 2.1. Basic Notations -- 2.2. Comparing Two Distributions: Entropy‐Based Measures and Stochastic Dominance Tests -- 2.2.1. Entropy‐Based Measures -- 2.2.2. Stochastic Dominance -- 2.3. Identification and Estimation of Counterfactual Distributions -- 2.3.1. Without Selection -- 2.3.2. Accounting for Selection -- 3. Data -- 4. Baseline Results -- 4.1. Female Wage Versus Counterfactual Distribution #1 -- 4.1.1. Entropy and Conventional Measures of the Differences -- 4.1.2. Stochastic Dominance Test Results -- 4.2. Female Wage Versus Counterfactual Distribution #2 -- 5. Results Addressing Selection -- 6. Conclusions -- References -- Part B3: Econometric Methodologies -- Chapter 10: Where (and by How Much) Does a Theory Break Down? With an Application to the Expectation Hypothesis -- 1. Introduction -- 2. The EH: Implied Relations and Previous Findings -- 3. NP Evidence: Where Do the Violations of the EH Come From? -- 4. The Last Straw: How Many Observations Cause the Rejection of the Hypothesis? -- 4.1. RLS on the Sorted Data -- 4.2. LS with Sequential Thresholds, on the Original Data -- 4.3. Further Discussion of the Methodology -- 5. Concluding Comments -- References -- Chapter 11: Gaussian Rank Correlation and Regression -- 1. Introduction 2. Theoretical Background -- 2.1. Econometric Model -- 2.2. The Score Vector and the Hessian Matrix -- 2.3. Partial Correlation and Regression -- 3. Asymptotic Properties under Correct Specification -- 3.1. When Margins Are Known -- 3.1.1. Information Matrix Equality -- 3.1.2. Asymptotic Distribution of the ML Estimators -- 3.2. Replacing Margins with Empirical cdf's -- 3.3. Efficiency Comparison with Other Moment Estimators -- 3.3.1. Correlation Measures -- 3.3.2. Partial Correlation Coefficients and Regression -- 4. Misspecification Analysis -- 4.1. Pseudo-true Values -- 4.2. Asymptotic Distribution -- 5. Comparison with Alternative Estimators -- 5.1. Spearman Correlation -- 5.2. Pearson Correlation -- 5.3. Comparison -- 6. Monte Carlo Evidence -- 6.1. Design and Estimation Details -- 6.2. Sampling Distribution of the Different Estimators -- 6.3. Finite Sample Inference -- 6.4. The Effect of Outliers -- 7. Empirical Applications -- 7.1. Migration and Growth Rates -- 7.2. The Augmented Solow Growth Model -- 8. Conclusions and Directions for Further Research -- References -- Appendix A.Some Practical Considerations -- Chapter 12: Robust Dynamic Panel Data Models Using ε-Contamination -- 1. Introduction -- 2. A Robust Linear Dynamic Panel Data Model -- 2.1. The Static Framework -- 2.2. The Dynamic Framework -- 2.3. The Robust Dynamic Linear Model in the Two-stage Hierarchy -- 2.3.1. The First Step of the Robust Bayesian Estimator -- 2.3.2. The Second Step of the Robust Bayesian Estimator -- 2.4. Estimating the ML-II Posterior Variance-Covariance Matrix -- 3. Monte Carlo Simulation Study -- 3.1. The DGP of the Monte Carlo Simulation Study -- 3.1.1. The random effects World -- 3.1.2. The Chamberlain-type fixed effects World -- 3.1.3. The Hausman-Taylor World -- 3.1.4. The Dynamic Homogeneous Panel Data World with Common Trends 3.1.5. The Dynamic Homogeneous Panel Data World with Common Correlated Effects -- 3.1.6. The Dynamic Heterogeneous Panel Data World with Common Correlated Effects -- 4. Conclusion -- Notes -- References -- Chapter 13: Identification‐robust Inference for Endogeneity Parameters in Models with an Incomplete Reduced Form -- 1. Introduction -- 2. Framework and Notation -- 2.1. Basic Structural Framework -- 2.2. Notation -- 3. Asymptotic Assumptions -- 4. Two‐stage inference for endogeneity covariances -- 4.1. Inference for Structural Parameter β -- 4.2. Conditional Point Estimation of Endogeneity Covariances -- 4.3. Joint Inference for Structural Parameters and Endogeneity Covariances -- 4.4. Projection‐based Inference for Endogeneity Covariances -- 5. Inference for Total Effect -- 6. Exogeneity Tests -- 7. Application to Return‐to‐Schooling Model -- 7.1. Data -- 7.2. Results -- 8. Conclusion -- References -- Index |
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Dynamic Response Path of Labor Participation Decision to a Health Shock -- Parameter Heterogeneity (Stability) of a State-Dependent Model -- Concluding Remarks -- References -- Appendix -- A Cohort Approach to Estimate Fixed Individual-Specific Effects for a Dynamic Binary State-Dependent Model -- Chapter 4: Multiple Treatment Effects in Panel-Heterogeneity and Aggregation -- 1. Introduction -- 2. Measurement of Treatment Effects and Sample Configuration -- 3. Homogeneous or Heterogeneous Treatment Effects -- 4. Aggregation Methods -- 5. To Aggregate or Not -- 5.1. Criterion -- 5.2. Test of Significance between the Aggregate and Disaggregate Approaches</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Test 1: Diebold and Mariano (1995) Asymptotic Test -- Test 2. The Sign Test -- Test 3. The Wilcoxon's Signed-Rank Test -- 6. Monte Carlo Simulation -- 7. Analysis of China's P2P Market -- 7.1. A Brief History -- 7.2. The Model and the Data -- 8. Conclusion -- References -- Chapter 5: Backward Mean Transformation in Panel Data with Predetermined Regressors -- 1. Introduction -- 2. The Model -- 3. Asymptotic Theory -- 3.1. Stationary Setup -- 3.2. Mean Non-stationarity -- 3.3. A Stylized Autoregressive Model -- 4. Other Approaches Based on Backward Means -- 4.1. Bias-Corrected OBM -- 4.2. OLS-IV Estimator of Everaert (2013) -- 4.3. Other Hybrid OLS-IV Estimators -- 4.4. Explanatory Notes on Everaert (2013) -- 5. Finite Sample Results -- 6. Concluding Remarks -- References -- Appendix. Tables: Monte Carlo Results for the Dynamic Model -- Chapter 6: Various Asymptotic Distributions of the Error-Components Test for Cross-Sectional Correlation -- Introduction -- 2. A Review on the Double-Indexed CLT -- 3. Testing for Cross-Sectional Correlation -- 3.1. The Conventional Test Statistic -- 3.2. A Modified Test Statistic -- 3.3. Simulating the Critical Values for Fixed T, Equation (17) -- 4. A Monte Carlo Experiment -- References -- Appendix: Technical Proofs -- Chapter 7: Trimmed Mean Group Estimation -- 1. Introduction -- 2. Motivation -- 2.1. Weighted Mean Group Estimation -- 2.2. Trimming Based on the Variances of Regressors -- 2.3. Examples -- 2.4. Trimming Weights -- 3. Trimmed Mean Group Estimation -- 3.1. Trimmed MG Estimator for Two‐Way FE Models -- 3.2. Trimmed CCEMG Estimator -- 4. Monte Carlo Simulation -- 5. Empirical Illustration: Effect of Police on Crime -- 6. Concluding Remarks -- References -- Part B2: Micro Modeling -- Chapter 8: Corporate Indebtedness and Low Productivity Growth of Italian Firms -- 1. Introduction -- 2. Data</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3. Descriptive Statistics -- 3.1. Coverage -- 3.2. Evolution of Firm Indebtedness -- 3.3. Firm Productivity -- 4. Empirical Framework -- 4.1. Auto-Regressive Distributed Lag (ARDL) Approach -- 4.1.1. Estimation and Panel Tests of Threshold Effects -- 4.2. Distributed Lag (DL) Approach -- 4.3. Cross-Sectionally Augmented ARDL and DL Approaches -- 5. Results -- 5.1. Estimates of Long-Run Effects -- 5.2. Tests of Corporate-Debt Threshold Effects -- 5.3. Economic Significance -- 6. Conclusion -- References -- Chapter 9: Women's Potential Earnings Distributions -- 1. Introduction -- 2. Empirical Methods -- 2.1. Basic Notations -- 2.2. Comparing Two Distributions: Entropy‐Based Measures and Stochastic Dominance Tests -- 2.2.1. Entropy‐Based Measures -- 2.2.2. Stochastic Dominance -- 2.3. Identification and Estimation of Counterfactual Distributions -- 2.3.1. Without Selection -- 2.3.2. Accounting for Selection -- 3. Data -- 4. Baseline Results -- 4.1. Female Wage Versus Counterfactual Distribution #1 -- 4.1.1. Entropy and Conventional Measures of the Differences -- 4.1.2. Stochastic Dominance Test Results -- 4.2. Female Wage Versus Counterfactual Distribution #2 -- 5. Results Addressing Selection -- 6. Conclusions -- References -- Part B3: Econometric Methodologies -- Chapter 10: Where (and by How Much) Does a Theory Break Down? With an Application to the Expectation Hypothesis -- 1. Introduction -- 2. The EH: Implied Relations and Previous Findings -- 3. NP Evidence: Where Do the Violations of the EH Come From? -- 4. The Last Straw: How Many Observations Cause the Rejection of the Hypothesis? -- 4.1. RLS on the Sorted Data -- 4.2. LS with Sequential Thresholds, on the Original Data -- 4.3. Further Discussion of the Methodology -- 5. Concluding Comments -- References -- Chapter 11: Gaussian Rank Correlation and Regression -- 1. Introduction</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2. Theoretical Background -- 2.1. Econometric Model -- 2.2. The Score Vector and the Hessian Matrix -- 2.3. Partial Correlation and Regression -- 3. Asymptotic Properties under Correct Specification -- 3.1. When Margins Are Known -- 3.1.1. Information Matrix Equality -- 3.1.2. Asymptotic Distribution of the ML Estimators -- 3.2. Replacing Margins with Empirical cdf's -- 3.3. Efficiency Comparison with Other Moment Estimators -- 3.3.1. Correlation Measures -- 3.3.2. Partial Correlation Coefficients and Regression -- 4. Misspecification Analysis -- 4.1. Pseudo-true Values -- 4.2. Asymptotic Distribution -- 5. Comparison with Alternative Estimators -- 5.1. Spearman Correlation -- 5.2. Pearson Correlation -- 5.3. Comparison -- 6. Monte Carlo Evidence -- 6.1. Design and Estimation Details -- 6.2. Sampling Distribution of the Different Estimators -- 6.3. Finite Sample Inference -- 6.4. The Effect of Outliers -- 7. Empirical Applications -- 7.1. Migration and Growth Rates -- 7.2. The Augmented Solow Growth Model -- 8. Conclusions and Directions for Further Research -- References -- Appendix A.Some Practical Considerations -- Chapter 12: Robust Dynamic Panel Data Models Using ε-Contamination -- 1. Introduction -- 2. A Robust Linear Dynamic Panel Data Model -- 2.1. The Static Framework -- 2.2. The Dynamic Framework -- 2.3. The Robust Dynamic Linear Model in the Two-stage Hierarchy -- 2.3.1. The First Step of the Robust Bayesian Estimator -- 2.3.2. The Second Step of the Robust Bayesian Estimator -- 2.4. Estimating the ML-II Posterior Variance-Covariance Matrix -- 3. Monte Carlo Simulation Study -- 3.1. The DGP of the Monte Carlo Simulation Study -- 3.1.1. The random effects World -- 3.1.2. The Chamberlain-type fixed effects World -- 3.1.3. The Hausman-Taylor World -- 3.1.4. The Dynamic Homogeneous Panel Data World with Common Trends</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.1.5. The Dynamic Homogeneous Panel Data World with Common Correlated Effects -- 3.1.6. The Dynamic Heterogeneous Panel Data World with Common Correlated Effects -- 4. Conclusion -- Notes -- References -- Chapter 13: Identification‐robust Inference for Endogeneity Parameters in Models with an Incomplete Reduced Form -- 1. Introduction -- 2. Framework and Notation -- 2.1. Basic Structural Framework -- 2.2. Notation -- 3. Asymptotic Assumptions -- 4. Two‐stage inference for endogeneity covariances -- 4.1. Inference for Structural Parameter β -- 4.2. Conditional Point Estimation of Endogeneity Covariances -- 4.3. Joint Inference for Structural Parameters and Endogeneity Covariances -- 4.4. Projection‐based Inference for Endogeneity Covariances -- 5. Inference for Total Effect -- 6. Exogeneity Tests -- 7. Application to Return‐to‐Schooling Model -- 7.1. Data -- 7.2. Results -- 8. Conclusion -- References -- Index</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The collection of chapters in Volume 43 Part B of Advances in Econometricsserves as a tribute to one of the most innovative, influential, and productive econometricians of his generation, Professor M. Hashem Pesaran</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Econometrics</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Makroökonomisches Modell</subfield><subfield code="0">(DE-588)4074486-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Prognose</subfield><subfield code="0">(DE-588)4047390-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Ökonometrie</subfield><subfield code="0">(DE-588)4132280-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4016928-5</subfield><subfield code="a">Festschrift</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Ökonometrie</subfield><subfield code="0">(DE-588)4132280-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Makroökonomisches Modell</subfield><subfield code="0">(DE-588)4074486-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Prognose</subfield><subfield code="0">(DE-588)4047390-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hsiao, Cheng</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Timmermann, Allan</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Chudik, Alexander</subfield><subfield code="t">Essays in Honor of M. Hashem Pesaran</subfield><subfield code="d">Bingley : Emerald Publishing Limited,c2022</subfield><subfield code="z">9781802620665</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035213645</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=6846509</subfield><subfield code="l">DE-2070s</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| genre | (DE-588)4016928-5 Festschrift gnd-content |
| genre_facet | Festschrift |
| id | DE-604.BV049874187 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-10T19:04:05Z |
| institution | BVB |
| isbn | 9781802620658 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035213645 |
| oclc_num | 1293241978 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (372 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Emerald Publishing Limited |
| record_format | marc |
| series2 | Advances in Econometrics Series |
| spelling | Chudik, Alexander Verfasser aut Essays in Honor of M. Hashem Pesaran Panel Modeling, Micro Applications, and Econometric Methodology 1st ed Bingley Emerald Publishing Limited 2022 ©2022 1 Online-Ressource (372 Seiten) txt rdacontent c rdamedia cr rdacarrier Advances in Econometrics Series v.43, Part B Description based on publisher supplied metadata and other sources Intro -- Half Title Page -- Series editors Page -- Title Page -- Copyright Page -- Contents -- Introduction -- Part B: Panel Modeling, Micro Applications, and Econometric Methodology -- Part B1. Panel Data Methods -- Part B2. Micro Modeling -- Part B3. Econometric Methodologies -- References -- Part B1: Panel Data Methods -- Chapter 1: A Panel Data Model With Generalized Higher-Order Network Effects -- 1. Introduction -- 2. Model -- 2.1. Notation and Outline -- 2.2. Key Assumptions -- 3. Monte Carlo simulations -- 3.1. Design -- 3.2. Results -- 3.3. Likelihood-Ratio Tests -- 4. Conclusions -- References -- Chapter 2: Spatial and Spatio-Temporal Error Correction, Networks and Common Correlated Effects -- 1. Introduction -- 2. Spatial and Spatio-Temporal Error Correction Models -- 3. Spatio-Temporal ECM, Common Correlated Effects and Weak Dependence -- 4. An Application to House Prices in the UK -- 4.1. Economic Model -- 4.2. Data -- 4.3. Discussion of Results -- 5. Conclusion -- Acknowledgments -- References -- Appendix: Estimation Steps -- Chapter 3: Heterogeneity and Dynamic Dependence in Panel Analysis of Individual Behavior -- 1. Introduction -- 2. Dynamic Response Path of Labor Participation Decision to a Health Shock -- Parameter Heterogeneity (Stability) of a State-Dependent Model -- Concluding Remarks -- References -- Appendix -- A Cohort Approach to Estimate Fixed Individual-Specific Effects for a Dynamic Binary State-Dependent Model -- Chapter 4: Multiple Treatment Effects in Panel-Heterogeneity and Aggregation -- 1. Introduction -- 2. Measurement of Treatment Effects and Sample Configuration -- 3. Homogeneous or Heterogeneous Treatment Effects -- 4. Aggregation Methods -- 5. To Aggregate or Not -- 5.1. Criterion -- 5.2. Test of Significance between the Aggregate and Disaggregate Approaches Test 1: Diebold and Mariano (1995) Asymptotic Test -- Test 2. The Sign Test -- Test 3. The Wilcoxon's Signed-Rank Test -- 6. Monte Carlo Simulation -- 7. Analysis of China's P2P Market -- 7.1. A Brief History -- 7.2. The Model and the Data -- 8. Conclusion -- References -- Chapter 5: Backward Mean Transformation in Panel Data with Predetermined Regressors -- 1. Introduction -- 2. The Model -- 3. Asymptotic Theory -- 3.1. Stationary Setup -- 3.2. Mean Non-stationarity -- 3.3. A Stylized Autoregressive Model -- 4. Other Approaches Based on Backward Means -- 4.1. Bias-Corrected OBM -- 4.2. OLS-IV Estimator of Everaert (2013) -- 4.3. Other Hybrid OLS-IV Estimators -- 4.4. Explanatory Notes on Everaert (2013) -- 5. Finite Sample Results -- 6. Concluding Remarks -- References -- Appendix. Tables: Monte Carlo Results for the Dynamic Model -- Chapter 6: Various Asymptotic Distributions of the Error-Components Test for Cross-Sectional Correlation -- Introduction -- 2. A Review on the Double-Indexed CLT -- 3. Testing for Cross-Sectional Correlation -- 3.1. The Conventional Test Statistic -- 3.2. A Modified Test Statistic -- 3.3. Simulating the Critical Values for Fixed T, Equation (17) -- 4. A Monte Carlo Experiment -- References -- Appendix: Technical Proofs -- Chapter 7: Trimmed Mean Group Estimation -- 1. Introduction -- 2. Motivation -- 2.1. Weighted Mean Group Estimation -- 2.2. Trimming Based on the Variances of Regressors -- 2.3. Examples -- 2.4. Trimming Weights -- 3. Trimmed Mean Group Estimation -- 3.1. Trimmed MG Estimator for Two‐Way FE Models -- 3.2. Trimmed CCEMG Estimator -- 4. Monte Carlo Simulation -- 5. Empirical Illustration: Effect of Police on Crime -- 6. Concluding Remarks -- References -- Part B2: Micro Modeling -- Chapter 8: Corporate Indebtedness and Low Productivity Growth of Italian Firms -- 1. Introduction -- 2. Data 3. Descriptive Statistics -- 3.1. Coverage -- 3.2. Evolution of Firm Indebtedness -- 3.3. Firm Productivity -- 4. Empirical Framework -- 4.1. Auto-Regressive Distributed Lag (ARDL) Approach -- 4.1.1. Estimation and Panel Tests of Threshold Effects -- 4.2. Distributed Lag (DL) Approach -- 4.3. Cross-Sectionally Augmented ARDL and DL Approaches -- 5. Results -- 5.1. Estimates of Long-Run Effects -- 5.2. Tests of Corporate-Debt Threshold Effects -- 5.3. Economic Significance -- 6. Conclusion -- References -- Chapter 9: Women's Potential Earnings Distributions -- 1. Introduction -- 2. Empirical Methods -- 2.1. Basic Notations -- 2.2. Comparing Two Distributions: Entropy‐Based Measures and Stochastic Dominance Tests -- 2.2.1. Entropy‐Based Measures -- 2.2.2. Stochastic Dominance -- 2.3. Identification and Estimation of Counterfactual Distributions -- 2.3.1. Without Selection -- 2.3.2. Accounting for Selection -- 3. Data -- 4. Baseline Results -- 4.1. Female Wage Versus Counterfactual Distribution #1 -- 4.1.1. Entropy and Conventional Measures of the Differences -- 4.1.2. Stochastic Dominance Test Results -- 4.2. Female Wage Versus Counterfactual Distribution #2 -- 5. Results Addressing Selection -- 6. Conclusions -- References -- Part B3: Econometric Methodologies -- Chapter 10: Where (and by How Much) Does a Theory Break Down? With an Application to the Expectation Hypothesis -- 1. Introduction -- 2. The EH: Implied Relations and Previous Findings -- 3. NP Evidence: Where Do the Violations of the EH Come From? -- 4. The Last Straw: How Many Observations Cause the Rejection of the Hypothesis? -- 4.1. RLS on the Sorted Data -- 4.2. LS with Sequential Thresholds, on the Original Data -- 4.3. Further Discussion of the Methodology -- 5. Concluding Comments -- References -- Chapter 11: Gaussian Rank Correlation and Regression -- 1. Introduction 2. Theoretical Background -- 2.1. Econometric Model -- 2.2. The Score Vector and the Hessian Matrix -- 2.3. Partial Correlation and Regression -- 3. Asymptotic Properties under Correct Specification -- 3.1. When Margins Are Known -- 3.1.1. Information Matrix Equality -- 3.1.2. Asymptotic Distribution of the ML Estimators -- 3.2. Replacing Margins with Empirical cdf's -- 3.3. Efficiency Comparison with Other Moment Estimators -- 3.3.1. Correlation Measures -- 3.3.2. Partial Correlation Coefficients and Regression -- 4. Misspecification Analysis -- 4.1. Pseudo-true Values -- 4.2. Asymptotic Distribution -- 5. Comparison with Alternative Estimators -- 5.1. Spearman Correlation -- 5.2. Pearson Correlation -- 5.3. Comparison -- 6. Monte Carlo Evidence -- 6.1. Design and Estimation Details -- 6.2. Sampling Distribution of the Different Estimators -- 6.3. Finite Sample Inference -- 6.4. The Effect of Outliers -- 7. Empirical Applications -- 7.1. Migration and Growth Rates -- 7.2. The Augmented Solow Growth Model -- 8. Conclusions and Directions for Further Research -- References -- Appendix A.Some Practical Considerations -- Chapter 12: Robust Dynamic Panel Data Models Using ε-Contamination -- 1. Introduction -- 2. A Robust Linear Dynamic Panel Data Model -- 2.1. The Static Framework -- 2.2. The Dynamic Framework -- 2.3. The Robust Dynamic Linear Model in the Two-stage Hierarchy -- 2.3.1. The First Step of the Robust Bayesian Estimator -- 2.3.2. The Second Step of the Robust Bayesian Estimator -- 2.4. Estimating the ML-II Posterior Variance-Covariance Matrix -- 3. Monte Carlo Simulation Study -- 3.1. The DGP of the Monte Carlo Simulation Study -- 3.1.1. The random effects World -- 3.1.2. The Chamberlain-type fixed effects World -- 3.1.3. The Hausman-Taylor World -- 3.1.4. The Dynamic Homogeneous Panel Data World with Common Trends 3.1.5. The Dynamic Homogeneous Panel Data World with Common Correlated Effects -- 3.1.6. The Dynamic Heterogeneous Panel Data World with Common Correlated Effects -- 4. Conclusion -- Notes -- References -- Chapter 13: Identification‐robust Inference for Endogeneity Parameters in Models with an Incomplete Reduced Form -- 1. Introduction -- 2. Framework and Notation -- 2.1. Basic Structural Framework -- 2.2. Notation -- 3. Asymptotic Assumptions -- 4. Two‐stage inference for endogeneity covariances -- 4.1. Inference for Structural Parameter β -- 4.2. Conditional Point Estimation of Endogeneity Covariances -- 4.3. Joint Inference for Structural Parameters and Endogeneity Covariances -- 4.4. Projection‐based Inference for Endogeneity Covariances -- 5. Inference for Total Effect -- 6. Exogeneity Tests -- 7. Application to Return‐to‐Schooling Model -- 7.1. Data -- 7.2. Results -- 8. Conclusion -- References -- Index The collection of chapters in Volume 43 Part B of Advances in Econometricsserves as a tribute to one of the most innovative, influential, and productive econometricians of his generation, Professor M. Hashem Pesaran Econometrics Makroökonomisches Modell (DE-588)4074486-3 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4016928-5 Festschrift gnd-content Ökonometrie (DE-588)4132280-0 s Makroökonomisches Modell (DE-588)4074486-3 s Prognose (DE-588)4047390-9 s DE-604 Hsiao, Cheng Sonstige oth Timmermann, Allan Sonstige oth Erscheint auch als Druck-Ausgabe Chudik, Alexander Essays in Honor of M. Hashem Pesaran Bingley : Emerald Publishing Limited,c2022 9781802620665 |
| spellingShingle | Chudik, Alexander Essays in Honor of M. Hashem Pesaran Panel Modeling, Micro Applications, and Econometric Methodology Intro -- Half Title Page -- Series editors Page -- Title Page -- Copyright Page -- Contents -- Introduction -- Part B: Panel Modeling, Micro Applications, and Econometric Methodology -- Part B1. Panel Data Methods -- Part B2. Micro Modeling -- Part B3. Econometric Methodologies -- References -- Part B1: Panel Data Methods -- Chapter 1: A Panel Data Model With Generalized Higher-Order Network Effects -- 1. Introduction -- 2. Model -- 2.1. Notation and Outline -- 2.2. Key Assumptions -- 3. Monte Carlo simulations -- 3.1. Design -- 3.2. Results -- 3.3. Likelihood-Ratio Tests -- 4. Conclusions -- References -- Chapter 2: Spatial and Spatio-Temporal Error Correction, Networks and Common Correlated Effects -- 1. Introduction -- 2. Spatial and Spatio-Temporal Error Correction Models -- 3. Spatio-Temporal ECM, Common Correlated Effects and Weak Dependence -- 4. An Application to House Prices in the UK -- 4.1. Economic Model -- 4.2. Data -- 4.3. Discussion of Results -- 5. Conclusion -- Acknowledgments -- References -- Appendix: Estimation Steps -- Chapter 3: Heterogeneity and Dynamic Dependence in Panel Analysis of Individual Behavior -- 1. Introduction -- 2. Dynamic Response Path of Labor Participation Decision to a Health Shock -- Parameter Heterogeneity (Stability) of a State-Dependent Model -- Concluding Remarks -- References -- Appendix -- A Cohort Approach to Estimate Fixed Individual-Specific Effects for a Dynamic Binary State-Dependent Model -- Chapter 4: Multiple Treatment Effects in Panel-Heterogeneity and Aggregation -- 1. Introduction -- 2. Measurement of Treatment Effects and Sample Configuration -- 3. Homogeneous or Heterogeneous Treatment Effects -- 4. Aggregation Methods -- 5. To Aggregate or Not -- 5.1. Criterion -- 5.2. Test of Significance between the Aggregate and Disaggregate Approaches Test 1: Diebold and Mariano (1995) Asymptotic Test -- Test 2. The Sign Test -- Test 3. The Wilcoxon's Signed-Rank Test -- 6. Monte Carlo Simulation -- 7. Analysis of China's P2P Market -- 7.1. A Brief History -- 7.2. The Model and the Data -- 8. Conclusion -- References -- Chapter 5: Backward Mean Transformation in Panel Data with Predetermined Regressors -- 1. Introduction -- 2. The Model -- 3. Asymptotic Theory -- 3.1. Stationary Setup -- 3.2. Mean Non-stationarity -- 3.3. A Stylized Autoregressive Model -- 4. Other Approaches Based on Backward Means -- 4.1. Bias-Corrected OBM -- 4.2. OLS-IV Estimator of Everaert (2013) -- 4.3. Other Hybrid OLS-IV Estimators -- 4.4. Explanatory Notes on Everaert (2013) -- 5. Finite Sample Results -- 6. Concluding Remarks -- References -- Appendix. Tables: Monte Carlo Results for the Dynamic Model -- Chapter 6: Various Asymptotic Distributions of the Error-Components Test for Cross-Sectional Correlation -- Introduction -- 2. A Review on the Double-Indexed CLT -- 3. Testing for Cross-Sectional Correlation -- 3.1. The Conventional Test Statistic -- 3.2. A Modified Test Statistic -- 3.3. Simulating the Critical Values for Fixed T, Equation (17) -- 4. A Monte Carlo Experiment -- References -- Appendix: Technical Proofs -- Chapter 7: Trimmed Mean Group Estimation -- 1. Introduction -- 2. Motivation -- 2.1. Weighted Mean Group Estimation -- 2.2. Trimming Based on the Variances of Regressors -- 2.3. Examples -- 2.4. Trimming Weights -- 3. Trimmed Mean Group Estimation -- 3.1. Trimmed MG Estimator for Two‐Way FE Models -- 3.2. Trimmed CCEMG Estimator -- 4. Monte Carlo Simulation -- 5. Empirical Illustration: Effect of Police on Crime -- 6. Concluding Remarks -- References -- Part B2: Micro Modeling -- Chapter 8: Corporate Indebtedness and Low Productivity Growth of Italian Firms -- 1. Introduction -- 2. Data 3. Descriptive Statistics -- 3.1. Coverage -- 3.2. Evolution of Firm Indebtedness -- 3.3. Firm Productivity -- 4. Empirical Framework -- 4.1. Auto-Regressive Distributed Lag (ARDL) Approach -- 4.1.1. Estimation and Panel Tests of Threshold Effects -- 4.2. Distributed Lag (DL) Approach -- 4.3. Cross-Sectionally Augmented ARDL and DL Approaches -- 5. Results -- 5.1. Estimates of Long-Run Effects -- 5.2. Tests of Corporate-Debt Threshold Effects -- 5.3. Economic Significance -- 6. Conclusion -- References -- Chapter 9: Women's Potential Earnings Distributions -- 1. Introduction -- 2. Empirical Methods -- 2.1. Basic Notations -- 2.2. Comparing Two Distributions: Entropy‐Based Measures and Stochastic Dominance Tests -- 2.2.1. Entropy‐Based Measures -- 2.2.2. Stochastic Dominance -- 2.3. Identification and Estimation of Counterfactual Distributions -- 2.3.1. Without Selection -- 2.3.2. Accounting for Selection -- 3. Data -- 4. Baseline Results -- 4.1. Female Wage Versus Counterfactual Distribution #1 -- 4.1.1. Entropy and Conventional Measures of the Differences -- 4.1.2. Stochastic Dominance Test Results -- 4.2. Female Wage Versus Counterfactual Distribution #2 -- 5. Results Addressing Selection -- 6. Conclusions -- References -- Part B3: Econometric Methodologies -- Chapter 10: Where (and by How Much) Does a Theory Break Down? With an Application to the Expectation Hypothesis -- 1. Introduction -- 2. The EH: Implied Relations and Previous Findings -- 3. NP Evidence: Where Do the Violations of the EH Come From? -- 4. The Last Straw: How Many Observations Cause the Rejection of the Hypothesis? -- 4.1. RLS on the Sorted Data -- 4.2. LS with Sequential Thresholds, on the Original Data -- 4.3. Further Discussion of the Methodology -- 5. Concluding Comments -- References -- Chapter 11: Gaussian Rank Correlation and Regression -- 1. Introduction 2. Theoretical Background -- 2.1. Econometric Model -- 2.2. The Score Vector and the Hessian Matrix -- 2.3. Partial Correlation and Regression -- 3. Asymptotic Properties under Correct Specification -- 3.1. When Margins Are Known -- 3.1.1. Information Matrix Equality -- 3.1.2. Asymptotic Distribution of the ML Estimators -- 3.2. Replacing Margins with Empirical cdf's -- 3.3. Efficiency Comparison with Other Moment Estimators -- 3.3.1. Correlation Measures -- 3.3.2. Partial Correlation Coefficients and Regression -- 4. Misspecification Analysis -- 4.1. Pseudo-true Values -- 4.2. Asymptotic Distribution -- 5. Comparison with Alternative Estimators -- 5.1. Spearman Correlation -- 5.2. Pearson Correlation -- 5.3. Comparison -- 6. Monte Carlo Evidence -- 6.1. Design and Estimation Details -- 6.2. Sampling Distribution of the Different Estimators -- 6.3. Finite Sample Inference -- 6.4. The Effect of Outliers -- 7. Empirical Applications -- 7.1. Migration and Growth Rates -- 7.2. The Augmented Solow Growth Model -- 8. Conclusions and Directions for Further Research -- References -- Appendix A.Some Practical Considerations -- Chapter 12: Robust Dynamic Panel Data Models Using ε-Contamination -- 1. Introduction -- 2. A Robust Linear Dynamic Panel Data Model -- 2.1. The Static Framework -- 2.2. The Dynamic Framework -- 2.3. The Robust Dynamic Linear Model in the Two-stage Hierarchy -- 2.3.1. The First Step of the Robust Bayesian Estimator -- 2.3.2. The Second Step of the Robust Bayesian Estimator -- 2.4. Estimating the ML-II Posterior Variance-Covariance Matrix -- 3. Monte Carlo Simulation Study -- 3.1. The DGP of the Monte Carlo Simulation Study -- 3.1.1. The random effects World -- 3.1.2. The Chamberlain-type fixed effects World -- 3.1.3. The Hausman-Taylor World -- 3.1.4. The Dynamic Homogeneous Panel Data World with Common Trends 3.1.5. The Dynamic Homogeneous Panel Data World with Common Correlated Effects -- 3.1.6. The Dynamic Heterogeneous Panel Data World with Common Correlated Effects -- 4. Conclusion -- Notes -- References -- Chapter 13: Identification‐robust Inference for Endogeneity Parameters in Models with an Incomplete Reduced Form -- 1. Introduction -- 2. Framework and Notation -- 2.1. Basic Structural Framework -- 2.2. Notation -- 3. Asymptotic Assumptions -- 4. Two‐stage inference for endogeneity covariances -- 4.1. Inference for Structural Parameter β -- 4.2. Conditional Point Estimation of Endogeneity Covariances -- 4.3. Joint Inference for Structural Parameters and Endogeneity Covariances -- 4.4. Projection‐based Inference for Endogeneity Covariances -- 5. Inference for Total Effect -- 6. Exogeneity Tests -- 7. Application to Return‐to‐Schooling Model -- 7.1. Data -- 7.2. Results -- 8. Conclusion -- References -- Index Econometrics Makroökonomisches Modell (DE-588)4074486-3 gnd Prognose (DE-588)4047390-9 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4074486-3 (DE-588)4047390-9 (DE-588)4132280-0 (DE-588)4016928-5 |
| title | Essays in Honor of M. Hashem Pesaran Panel Modeling, Micro Applications, and Econometric Methodology |
| title_auth | Essays in Honor of M. Hashem Pesaran Panel Modeling, Micro Applications, and Econometric Methodology |
| title_exact_search | Essays in Honor of M. Hashem Pesaran Panel Modeling, Micro Applications, and Econometric Methodology |
| title_full | Essays in Honor of M. Hashem Pesaran Panel Modeling, Micro Applications, and Econometric Methodology |
| title_fullStr | Essays in Honor of M. Hashem Pesaran Panel Modeling, Micro Applications, and Econometric Methodology |
| title_full_unstemmed | Essays in Honor of M. Hashem Pesaran Panel Modeling, Micro Applications, and Econometric Methodology |
| title_short | Essays in Honor of M. Hashem Pesaran |
| title_sort | essays in honor of m hashem pesaran panel modeling micro applications and econometric methodology |
| title_sub | Panel Modeling, Micro Applications, and Econometric Methodology |
| topic | Econometrics Makroökonomisches Modell (DE-588)4074486-3 gnd Prognose (DE-588)4047390-9 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometrics Makroökonomisches Modell Prognose Ökonometrie Festschrift |
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