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December 2018 Extending Bayesian structural time-series estimates of causal impact to many-household conservation initiatives
Eric Schmitt, Christopher Tull, Patrick Atwater
Ann. Appl. Stat. 12(4): 2517-2539 (December 2018). DOI: 10.1214/18-AOAS1166

Abstract

Government agencies offer economic incentives to citizens for conservation actions, such as rebates for installing efficient appliances and compensation for modifications to homes. The intention of these conservation actions is frequently to reduce the consumption of a utility. Measuring the conservation impact of incentives is important for guiding policy but doing so is technically difficult. However, the methods for estimating the impact of public outreach efforts have seen substantial developments in marketing to consumers in recent years as marketers seek to substantiate the value of their services. One such method uses Bayesian Stuctural Time Series (BSTS) to compare a market exposed to an advertising campaign with control markets identified through a matching procedure. This paper introduces an extension of the matching/BSTS method for impact estimation to make it applicable for general conservation program impact estimation when multihousehold data is available. This is accomplished by household matching/BSTS steps to obtain conservation estimates and then aggregating the results using a meta-regression step to aggregate the findings. A case study examining the impact of rebates for household turf removal on water consumption in multiple Californian water districts is conducted to illustrate the work flow of this method.

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Eric Schmitt. Christopher Tull. Patrick Atwater. "Extending Bayesian structural time-series estimates of causal impact to many-household conservation initiatives." Ann. Appl. Stat. 12 (4) 2517 - 2539, December 2018. https://doi.org/10.1214/18-AOAS1166

Information

Received: 1 August 2017; Revised: 1 January 2018; Published: December 2018
First available in Project Euclid: 13 November 2018

zbMATH: 07029464
MathSciNet: MR3875710
Digital Object Identifier: 10.1214/18-AOAS1166

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 4 • December 2018
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