Bayesian Analysis of Demand Under Block Rate Pricing (eBook)
IX, 112 Seiten
Springer Singapore (Verlag)
978-981-15-1857-7 (ISBN)
This book focuses on the structural analysis of demand under block rate pricing, a type of nonlinear pricing used mainly in public utility services. In this price system, consumers are presented with several unit prices, which makes a naive analysis biased. However, the response to the price schedule is often of interest in economics and plays an important role in policymaking. To address this issue, the book adopts a structural approach, referred to as the discrete/continuous choice approach in the literature, to develop corresponding statistical models for analysis.
The resulting models are extensions of the Tobit model, a well-known statistical model in econometrics, and their hierarchical structure fits well in Bayesian methodology. Thus, the book takes the Bayesian approach and develops the Markov chain Monte Carlo method to conduct statistical inferences. The methodology derived is then applied to real-world datasets, microdata collected in Tokyo and the neighboring Chiba Prefecture, as a useful empirical analysis for prediction as well as policymaking.Koji Miyawaki, Kwansei Gakuin University
This book focuses on the structural analysis of demand under block rate pricing, a type of nonlinear pricing used mainly in public utility services. In this price system, consumers are presented with several unit prices, which makes a naive analysis biased. However, the response to the price schedule is often of interest in economics and plays an important role in policymaking. To address this issue, the book adopts a structural approach, referred to as the discrete/continuous choice approach in the literature, to develop corresponding statistical models for analysis.The resulting models are extensions of the Tobit model, a well-known statistical model in econometrics, and their hierarchical structure fits well in Bayesian methodology. Thus, the book takes the Bayesian approach and develops the Markov chain Monte Carlo method to conduct statistical inferences. The methodology derived is then applied to real-world datasets, microdata collected in Tokyo and the neighboring Chiba Prefecture, as a useful empirical analysis for prediction as well as policymaking.
| Erscheint lt. Verlag | 16.12.2019 |
|---|---|
| Reihe/Serie | JSS Research Series in Statistics |
| JSS Research Series in Statistics | |
| JSS Research Series in Statistics | |
| SpringerBriefs in Statistics | SpringerBriefs in Statistics |
| Zusatzinfo | IX, 112 p. 33 illus., 11 illus. in color. |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Recht / Steuern ► Wirtschaftsrecht | |
| Wirtschaft ► Allgemeines / Lexika | |
| Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| Schlagworte | Bayesian analysis • MCMC • Model Coherency • Public Utility Demand • Structural approach |
| ISBN-10 | 981-15-1857-2 / 9811518572 |
| ISBN-13 | 978-981-15-1857-7 / 9789811518577 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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