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Panel Data Econometrics: Theory

Editat de Mike Tsionas
Notă GoodReads:
en Limba Engleză Paperback – 18 Jun 2019
Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made.


  • Provides a vast array of empirical applications useful to practitioners from different application environments
  • Accompanied by extensive case studies and empirical exercises
  • Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings
  • Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts
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Specificații

ISBN-13: 9780128143674
ISBN-10: 0128143673
Pagini: 432
Dimensiuni: 152 x 229 x 24 mm
Greutate: 0.58 kg
Editura: ELSEVIER SCIENCE

Public țintă

Early career researchers in econometrics and other fields including banking, financial markets, tourism and transportation, auctions, experimental economics who seek to adopt econometric techniques for research in their specific application environments. Practitioners seeking a stronger footing for empirical studies. Graduate students and 1st year PhD students of economics, econometrics, and statistics looking to implement the formal skillset learned in volume one

Cuprins

1. A synopsis of econometrics
2. Testing and correcting for endogeneity in nonlinear unobserved effects models
3. Nonlinear and related panel data models
4. Nonparametric estimation and inference for panel data models
5. Heterogeneity and endogeneity in panel stochastic frontier models
6. Bayesian estimation of panel count data models: dynamics, latent heterogeneity, serial error correlation, and nonparametric structures
7. Fixed effects likelihood approach for large panels
8. Panel vector autoregressions with binary data
9. Implementing generalized panel data stochastic frontier estimators
10. Panel cointegration techniques and open challenges
11. Alternative approaches to the econometrics of panel data
12. Analysis of panel data using R