Buch, Englisch, Band 54, 551 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Advanced Studies in Theoretical and Applied Econometrics
Theory and Applications
Buch, Englisch, Band 54, 551 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Advanced Studies in Theoretical and Applied Econometrics
ISBN: 978-3-031-49851-0
Verlag: Springer
This second extended and revised edition provides an update of all existent chapters to reflect on new developments in the area as well as several new chapters on topics such as machine learning, nonparametric models,networks, and multi-dimensional panels in health economics. The book serves as a standard reference work, a textbook for graduate students in economics, and a source of background material for professionals conducting empirical studies.
Zielgruppe
Graduate
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
- Mathematik | Informatik Mathematik Operations Research
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
Weitere Infos & Material
Fixed Effects Models.- When and How Much Do Fixed Effects Matter?- Random Effects Models.- Estimation of Sparse Variance-Covariance Matrix.- Models with Endogenous Regressors.- Dynamic Models and Reciprocity.- Random Coefficients Models.- Nonparametric Models with Random Effects.- Nonparametric Models with Fixed Effects.- Multi-dimensional Panels in Quantile Regression Models.- Multi-dimensional Models for Spatial Panels.- The Econometrics of Gravity Models in International Trade.- Modelling Housing Using Multi-dimensional Panel Data.- Modelling Migration.- Multi-dimensional Panels in Health Economics with an Application on Antibiotic Consumption.- Can Machine Learning Beat Gravity in Flow Prediction?