Essays in Honor of Subal Kumbhakar

It is the editor's distinct privilege to gather this collection of papers that honors Subhal Kumbhakar's many accomplishments, drawing further attention to the various areas of scholarship that he has touched.

Intro -- Half Title Page -- Series Page -- Title Page -- Copyright Page -- Dedication Page -- Contents -- List of Contributors -- Chapter 1: Editors' Introduction -- References -- Letter from Dennis J. Aigner -- Chapter 2: Hausman's Specification Test for Panel Data: Practical Tips -- 1. Introduction1 -- 2. When Do I Settle on the Fixed Effects Estimator If the Hausman Test Rejects the Null? -- 3. The Mundlak (1978) and the Hausman and Taylor (1981) Estimators -- 4. Conclusion -- References -- Chapter 3: Do Federal Disability Insurance Participants Exaggerate Their Health Problems? A Study Using Anchoring Vignettes -- 1. Introduction -- 2. Data -- 3. The Model -- 4. Estimation Results -- 4.1. The Effects of DIF Correction -- 4.2. Threshold Analysis -- 5. Conclusions -- References -- Chapter 4: Homelessness on the West Coast and the Role of Health: Inefficiency and Productivity Loss in American Society -- 1. Introduction -- 1.2. Homelessness and Its Correlates in America1 -- 2. Previous Studies of Homelessness -- 2.1. Homelessness in America -- 2.2. Income and Homelessness -- 2.3. Health and Homelessness -- 2.3.1. Health Production and Future Outcomes -- 2.4. Spatial Analyses of Homelessness -- 3. Economic Theory -- 4. Data Sources -- 4.2. Healthcare Data -- 4.3. Income and Demographic Data -- 4.4. Reconciling Homeless Counts with Other Data -- 5. Econometric Model and Empirical Findings -- 5.1. Fixed Effects Analysis -- 5.2. Spatial Analysis -- 6. Results -- 6.2. Spatial Analysis -- 6.2.1. Analysis of SEM Propagation Due to an Unforeseen Shock such as COVID-19 Contagion -- 7. Conclusions -- References -- Chapter 5: Bootstrap Model Averaging Unit Root Inference -- 1. Introduction -- 2. Testing for a Unit Root -- 3. A Model Averaging Bootstrap Procedure -- 3.2. A Unit Root Model Average Bootstrap Procedure.

4. Monte Carlo Simulation for Bootstrap Unit Root Test -- 4.1. AR(1) DGP -- 4.2. ARMA(1,1) DGP -- 4.3. ARMA(1,2) DGP -- 4.4. Discussion -- 5. Conclusion -- References -- Chapter 6: Averaging Heterogeneous Autoregression Models with Heteroskedastic Errors: Theory and an Application to Cryptocurrency Volatility Forecasting -- 1. Introduction -- 2. A Review of Reference Models to Forecast Volatility -- 3. Asymptotic Optimality of the H-MAHAR Estimator -- 3.1. Asymptotic Optimality of H-MAHAR -- 4. Data and Simulation Exercise -- 4.1. Simulation Exercise -- 5. Empirical Application -- 6. Conclusion -- References -- Chapter 7: Efficient Estimation in Varying Coefficient Panel Data Model with Different Smoothing Variables and Fixed Effects -- 1. Introduction -- 2. Estimation of the Varying Coefficient Panel Data Model with Fixed Effect and Different Smoothing Variables -- 3. Asymptotic Properties -- 4. Monte Carlo Simulation -- 4.2. A Test for the Significance of the Coefficient Function -- 5. Semiparametric Estimation of Production Frontier and Inefficiency -- 6. Conclusion -- References -- Appendix -- Chapter 8: The Role of Management in Efficient Production: Theoretical and Statistical Implications -- 1. Introduction -- 2. Static Modeling of the Firm -- 3. A Dynamic Problem of the Firm -- 4. Dynamic Analysis Near the Steady State -- 5. Empirical Implications -- 5.2. Time-Invariant Management -- 6. Empirical Analysis -- 7. Conclusions and Implications for Empirical Work -- References -- Chapter 9: A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity -- 1. Introduction -- 2. A Model of Firm Production -- 3. Identification -- 3.1. A System Approach to Identification -- 3.2. Unidentification of the Standard Proxy Approach -- 4. Estimation Procedure -- 5. Finite-Sample Performance -- 5.1. Simulations.

5.2. Empirical Illustration -- 6. Extension to Imperfect Competition -- 7. Concluding Remarks -- Acknowledgments -- References -- Appendix 1. Identification Under the CES Specification -- Appendix 2. Expanded Ψ(a) from (3.16) -- Appendix 3. Semiparametric Sieve Estimation -- Appendix 4. Bootstrap Inference -- Appendix 5. Data Summary -- Appendix 6. Additional Empirical Results -- Appendix 7. Extension to Imperfect Competition -- Point Non-Identification of the Production Function -- Partial Identification of the Production Function -- Appendix 8. Proofs -- Proof of Proposition 1 -- Proof of Proposition 2 -- Chapter 10: The Structural and Productivity Effects of Infrastructure Provision in Developed and Developing Countries -- 1. Introduction -- 2. Diewert's (2015) TFP Decomposition -- 3. Decomposing the Within and Reallocation Effects -- 3.1. Decomposing the WE -- 3.2. Decomposing the OPE -- 3.3. Decomposing the Input Price Reallocation Effect -- 4. Sample and Data -- 5. Results -- 5.2. First-Level Decomposition -- 5.3. Second-Level Decomposition -- 5.3.1. Decomposing the WE -- 5.3.2. Decomposing the OPE -- 5.3.3. Decomposing the Input Price Reallocation Effect -- 5.3.4. Discussion -- 6. Conclusions -- References -- Chapter 11: Improving Predictions of Technical Inefficiency -- 1. Introduction -- 2. Estimators of Technical Inefficiency -- 2.2. The APS14 Estimator -- 2.3. The APS16 Estimator -- 2.4. Nadaraya-Watson -- 2.5. Local Linear Forests -- 3. Simulations -- 3.2. Model with Allocative Inefficiency -- 4. Empirical Illustration -- 5. Conclusion -- References -- Chapter 12: A Semiparametric Constant Elasticity of Substitution Stochastic Frontier Model for Panel Data -- 1. Introduction -- 2. Semiparametric CES Stochastic Frontier Model -- 2.2. Identification -- 2.3. Estimation -- 2.4. Basic Assumptions for Estimator Consistency.

3. Finite-Sample Performance -- 4. Empirical Application -- 5. Conclusion -- Acknowledgments -- References -- Appendix 1. Incidental Parameters Problem -- Appendix 2. Demonstration of our R Package -- Chapter 13: Random Versus Explained Inefficiency in Stochastic Frontier Analysis: The Case of Queensland Hospitals -- 1. Introduction -- 2. Background on Healthcare and Hospital Efficiency -- 3. SFA Models -- 3.2. SFA Models with Determinants of Inefficiency -- 4. Data, Variables, and Model Specification -- 4.2. Variables and Data Processing -- 4.3. Model Specification -- 5. Results and Discussion -- 5.2. Production Relationship -- 5.3. Model Selection -- 5.4. Inefficiency Function -- 5.5. Recovery Test of the Selected Model -- 5.6. Inefficiency Estimation of Individual Hospitals -- 5.7. Impact of the Environmental Variables -- 6. Concluding Remarks -- References -- Appendix 1. Aggregation of Variables -- Appendix 2. Trimming Outliers -- Appendix 3. Bibliometric Analysis of Selected SFMs -- Appendix 4. Degeneracy Diagnostics -- Appendix 5. Model Selection with Trend Variables -- Appendix 6. Results for the Selected Panel Data SFMs33 -- Chapter 14: Indirect Inference of Stochastic Frontier Models -- 1. Introduction -- 2. MSL Estimation -- 3. The Indirect Inference -- 4. Properties of the Indirect Inference Estimator -- 5. Prediction of Inefficiencies and TEs -- 6. Monte Carlo Simulations -- 6.1. Finite Sample Performance of the II Estimator -- 6.2. Comparison of the II, ML, and MSL Estimators -- 7. Conclusion -- References -- Chapter 15: The Nash Bargaining Two-tier Stochastic Frontier Model -- 1. Introduction -- 2. The Nash Bargaining Framework in Bilateral Negotiations -- 2.1. The Deterministic Setup -- 2.2. Nash Bargaining Under Productivity Uncertainty and Asymmetric Information.

2.2.1. The Surplus Functions of the Parties During the Negotiation Process and the Modified Nash Bargaining Solution -- 2.2.2. The Common-information Expectation, and the Target Wages of the Worker and the Firm -- 2.2.3. The Nash Bargaining Solution -- 2.3. Applicability in Other Bargaining Situations -- 2.4. The Two-tier Bilateral Nash Bargaining Stochastic Frontier Model -- 3. Statistical Specification I: Intra-error Dependence and Regressor Endogeneity -- 3.1. The 2TSF Bivariate Truncated Normal Specification -- 3.2. Handling Regressor Endogeneity Through Copulas -- 3.3. Empirical Application: Zambia Wages in the Early 1990s -- 3.4. Results on Endogeneity and Intra-error Dependence -- 3.5. Bargaining and Negotiation Effects -- 3.6. Wage Frontiers and Evolution Over Time -- 4. Statistical Specification II: Identification of Relative Bargaining Power -- 4.1. Empirical Illustration: Wage Bargaining in Ghana -- 5. Conclusions and Further Research -- References.