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Produktbild: Advances in Applied Econometrics
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Advances in Applied Econometrics Celebrating Peter Schmidt's Legacy

Aus der Reihe Advanced Studies in Theoretical and Applied Econometrics
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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.01.2026

Herausgeber

Subal C. Kumbhakar + weitere

Verlag

Springer

Seitenzahl

777

Maße (L/B/H)

23,5/15,5/4,2 cm

Gewicht

1171 g

Sprache

Englisch

ISBN

978-3-031-48387-5

Beschreibung

Portrait

Subal C Kumbhakar is a distinguished professor in economics at SUNY Binghamton, USA. He is a co-editor of the Springer journal Empirical Economics . He is a fellow of the Journal of Econometrics and a distinguished author of the Journal of Applied Econometrics . He holds an Honorary Doctorate degree from Gothenburg University, Sweden. He has extensively published in international journals in economics and econometrics.

Robin C. Sickles has published extensively in leading journals in economics and econometrics. He is a Fellow of the Journal of Econometrics , Elsevier Handbook Series in Economics, and International Association of Applied Econometrics and is a member of Conference on Research in Income and Wealth (NBER).

Hung-Jen Wang is a distinguished professor of National Taiwan University. His research interests are productivity and efficiency analysis, empirical macroeconomics, and monetary policy. He is an associate editor of the Journal of Productivity Analysis , Co-Editor of Taiwan Economics Forecast and Policy , and President of the Taiwan Economics Society.



Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.01.2026

Herausgeber

Verlag

Springer

Seitenzahl

777

Maße (L/B/H)

23,5/15,5/4,2 cm

Gewicht

1171 g

Sprache

Englisch

ISBN

978-3-031-48387-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Advances in Applied Econometrics

  • 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.