Advances in Applied Econometrics : Celebrating Peter Schmidt's Legacy

Chapter 1. Introduction -- Chapter 2. Robust Dynamic Space–time Panel Data Models Using εε-contamination: An Application to Crop Yields and Climate Change -- Chapter 3. Unbiased Estimation of the OLS Covariance Matrix When the Errors are Clustered -- Chapter 4. Refined GMM Estimators for Simultaneous Equations Models with Network Interactions -- Chapter 5. Identification and Estimation of Categorical Random Coefficient Models -- Chapter 6. Dynamic Panel GMM Estimators with Improved Finite Sample Properties using Parametric Restrictions for Dimension Reduction -- Chapter 7. Testing for Correlation Between the Regressors and Factor Loadings in Heterogeneous Panels with Interactive Effects -- Chapter 8. Assessing the Impacts of Pandemic and the Increase in Minimum Down Payment Rate on Shanghai Housing Prices -- Chapter 9. A Simple, Robust Test for Choosing the Level of Fixed Effects in Linear Panel Data Models -- Chapter 10. Internal Adjustment Costs of Firm-specific Factors and the Neoclassical Theory of the Firm -- Chapter 11. Proportional Incremental Cost Probability Functions and Their Frontiers -- Chapter 12. Hotelling Tubes, Confidence Bands and Conformal Inference -- Chapter 13. Indirect Inference Estimation of Stochastic Production Frontier Models With Skew-normal Noise -- Chapter 14. The Noise Error Component in Stochastic Frontier Analysis -- Chapter 15. An Alternative Corrected Ordinary Least Squares Estimator for the Stochastic Frontier Model -- Chapter 16. Likelihood-based Inference for Dynamic Panel Data Models -- Chapter 17. Approximating Long-memory Processes With Low-order Autoregressions: Implications for Modeling Realized Volatility -- Chapter 18. Does Climate Change Affect Economic Data? -- Chapter 19. Information Loss in Volatility Measurement With Flat Price Trading -- Chapter 20. Forecasting in the Presence of in-sample and Out-of-sample Breaks -- Chapter 21. Multivariate Models of Commodity Futures Markets: A Dynamic Copula Approach -- Chapter 22. Generalized Kernel Regularized Least Squares Estimator With Parametric Error Covariance -- Chapter 23. Predicting Binary Outcomes Based on the Pair-copula Construction -- Chapter 24. Public Subsidies and Innovation: a Doubly Robust Machine Learning Approach Leveraging Deep Neural Networks -- Chapter 25. DS-HECK: Double-lasso Estimation of Heckman Selection Model -- Chapter 26. Simultaneity in Binary Outcome Models with an Application to Employment for Couples.