Overview
- Presents the Bayesian estimation method for the discrete/continuous choice approach for demand under block rate pricing
- Explains the model coherency inherent in discrete/continuous choice and its connection to microeconomic theory
- Applies the estimation method to real-world datasets for the analysis of demand under block rate pricing, which can be used for prediction as well as policymaking
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Part of the book sub series: JSS Research Series in Statistics (JSSRES)
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Table of contents (6 chapters)
Keywords
About this book
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.Authors and Affiliations
About the author
Bibliographic Information
Book Title: Bayesian Analysis of Demand Under Block Rate Pricing
Authors: Koji Miyawaki
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-981-15-1857-7
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019
Softcover ISBN: 978-981-15-1856-0Published: 21 January 2020
eBook ISBN: 978-981-15-1857-7Published: 16 December 2019
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: IX, 112
Number of Illustrations: 22 b/w illustrations, 11 illustrations in colour
Topics: Statistics for Business, Management, Economics, Finance, Insurance, Statistical Theory and Methods, Bayesian Inference, Financial Engineering, R & D/Technology Policy