02504nmm a2200349 u 4500001001200000003002700012005001700039007002400056008004100080020001800121100001900139245010000158250001700258260005300275300006200328505020900390653003500599653002500634653002300659653001700682653002600699653007000725653002600795653004800821041001900869989003600888490003800924028003000962856007200992082001201064520107801076EB001888942EBX0100000000000000105230300000000000000.0cr|||||||||||||||||||||200117 ||| eng a97898115185771 aMiyawaki, Koji00aBayesian Analysis of Demand Under Block Rate PricinghElektronische Ressourcecby Koji Miyawaki a1st ed. 2019 aSingaporebSpringer Nature Singaporec2019, 2019 aIX, 112 p. 33 illus., 11 illus. in colorbonline resource0 a1. 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 aStatistical Theory and Methods aEconomic development aBayesian Inference aStatisticsĀ aFinancial engineering aStatistics in Business, Management, Economics, Finance, Insurance aFinancial Engineering aEconomic Development, Innovation and Growth07aeng2ISO 639-2 bSpringeraSpringer eBooks 2005-0 aJSS Research Series in Statistics50a10.1007/978-981-15-1857-740uhttps://doi.org/10.1007/978-981-15-1857-7?nosfx=yxVerlag3Volltext0 a300,727 aThis 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