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Bayesian analysis of demand under bl...

Miyawaki, Koji.

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Bayesian analysis of demand under block rate pricing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian analysis of demand under block rate pricing/ by Koji Miyawaki.
作者:	Miyawaki, Koji.
出版者:	Singapore :Springer Singapore : : 2019.,
面頁冊數:	ix, 112 p. :ill., digital ;24 cm.
內容註:	1. Introduction -- 2. Demand under Increasing Block Rate Pricing -- 3. Demand under Decreasing Block Rate Pricing -- 4. Extensions to Panel Data -- 5. Extensions to Areal Data -- 6. Block Normal Simulator.
Contained By:	Springer eBooks
標題:	Bayesian statistical decision theory. -
電子資源:	https://doi.org/10.1007/978-981-15-1857-7
ISBN:	9789811518577

Bayesian analysis of demand under block rate pricing
Miyawaki, Koji.

Bayesian analysis of demand under block rate pricing[electronic resource] /by Koji Miyawaki. - Singapore :Springer Singapore :2019. - ix, 112 p. :ill., digital ;24 cm. - SpringerBriefs in statistics. JSS research series in statistics. - SpringerBriefs in statistics.JSS research series in statistics..

1. Introduction -- 2. Demand under Increasing Block Rate Pricing -- 3. Demand under Decreasing Block Rate Pricing -- 4. Extensions to Panel Data -- 5. Extensions to Areal Data -- 6. Block Normal Simulator.

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.

ISBN: 9789811518577

Standard No.: 10.1007/978-981-15-1857-7doiSubjects--Topical Terms:

551404
Bayesian statistical decision theory.

LC Class. No.: QA279.5 / .M59 2019

Dewey Class. No.: 519.542

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