Empowering Consumers Through Data and Smart Technology: Experimental Evidence on the Consequences of Time‐of‐Use Electricity Pricing Policies

Harding, Matthew; Lamarche, Carlos

doi:10.1002/pam.21928

Cited by 49 publications

(43 citation statements)

References 26 publications

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“…Points show daily peak demand reduction potential during a peak hour of 6 PM. reduction of cooling electricity usage during these hours [44,45]. We base the shift measure on a previous study [46], which simulates a mechanical precooling measure in a home with thermal performance typical of new construction.…”

Section: Time-sensitive Impacts Of Efficiency and Flexibility Measuresmentioning

confidence: 99%

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

Satre-Meloy

Langevin

2019

Environ. Res. Lett.

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The building sector consumes 75% of US electricity, offering substantial energy, cost, and CO 2 emissions savings potential. New technologies enable buildings to flexibly manage electric loads across different times of day and season in support of a low-cost, low-carbon electric grid. Assessing the value of such technologies requires an understanding of building electric load variability at a higher temporal resolution than is demonstrated in previous studies of US building efficiency potential. We adapt Scout, an open-access model of US building energy use, to characterize sub-annual variations in baseline building electricity use, costs, and emissions at the national scale. We apply this baseline in time-sensitive analyses of the energy, cost, and CO 2 emissions savings potential of various degrees of energy efficiency and flexibility, finding that efficiency continues to have strong value in a timesensitive assessment framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.

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Section: Time-sensitive Impacts Of Efficiency and Flexibility Measuresmentioning

confidence: 99%

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

Satre-Meloy

Langevin

2019

Environ. Res. Lett.

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“…Thus, as well as implementing poor default choices for low-income enrollees, the program has driven up prices (Decarolis, 2015). 43 Evidence from the introduction of smart thermostats shows that consumers can be happy to automate consumption choices and that doing so can substantially affect energy use (Harding and Lamarche, 2015). 44 From the perspective of firms, it seems that automatic switching should be comparable to eliminating search costs, consumer price confusion, and switching costs all at once.…”

Section: Choosing For Consumers Defaults and Automatic Switchingmentioning

confidence: 99%

Failing to Choose the Best Price: Theory, Evidence, and Policy

Grubb

2015

Rev Ind Organ

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Both the "law of one price" and Bertrand's (1883) prediction of marginal cost pricing for homogeneous goods rest on the assumption that consumers will choose the best price. In practice, consumers often fail to choose the best price because they search too little, become confused comparing prices, and then show excessive inertia through too little switching away from past choices or default options. This is particularly true when price is a vector rather than a scalar, and consumers have limited experience in the relevant market. All three mistakes may contribute to positive markups that fail to diminish as the number of competing sellers increases. Firms may have an incentive to exacerbate these problems by obfuscating prices, thereby using complexity to make price comparisons difficult and soften competition. Possible regulatory interventions include simplifying the choice environment, for instance by restricting price to be a scalar, advising consumers of their expected costs under each option, or choosing on behalf of consumers. * Boston College, michael.grubb@bc.edu, https://www2.bc.edu/michael-grubb/. I am grateful to Mark Armstrong, Ben Handel, and Rani Spiegler for careful reading and many helpful comments on an earlier draft. I also thank Vera Sharunova for her excellent research assistance.

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“…This condition is used in a number of different approaches that have been recently developed for the estimation of panel quantile regression models. The recent literature include work by Koenker (2004), Lamarche (2010), , Rosen (2012), Galvao, Lamarche and Lima (2013), Chernozhukov, Fernandez-Val, Hahn and Newey (2013) and Chernozhukov, Fernandez-Val, Hoderlein, Holzmann and Newey (2015), Lamarche (2014, 2017), Arellano and Bonhomme (2016), among others. Slope heterogeneity in quantile regression is investigated in .…”

Section: Introductionmentioning

confidence: 99%

“…With the exception of and Arellano and Bonhomme (2016), the literature has focused on estimating static models. Moreover, the panel quantile regression literature does not address crosssectional dependence with the exception of Harding and Lamarche (2014) that adopt the approach proposed by Pesaran (2006) to estimate a static model with interactive effects. This paper extends the panel quantile literature to dynamic models with heterogeneous slopes and multi-factor error structure when both T and N are large.…”

Section: Introductionmentioning

confidence: 99%

“…The first part of Assumption 8 imposes compactness over the parameter space and it can be relaxed since the quantile objective functions corresponding to equations (2.9) and its approximate version (2.17) are convex in parameters. The second part is an identification condition that requires full rank and continuity as in Chernozhukov and Hansen (2006) and Harding and Lamarche (2014).…”

Section: Introductionmentioning

confidence: 99%

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Common Correlated Effects Estimation of Heterogeneous: Dynamic Panel Quantile Regression Models

2018

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This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed by Pesaran (2006) and Chudik and Pesaran (2015) and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. We establish consistency and derive the asymptotic distribution of the new quantile regression estimator. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time-of-Use pricing using a large randomized control trial. JEL-Codes: C210, C310, C330, D120, L940. The paper is organized as follows. The next section introduces the model and the proposed estimator. It also establishes the asymptotic properties of the estimator. Section 3 provides simulation experiments to investigate the small sample performance of the proposed estimator. Section 4 demonstrates how the estimator can be used in practice by exploring an application of electricity pricing and smart technology. Section 5 concludes. Mathematical proofs are provided in the Appendix and additional Monte Carlo results are offered in an online Supplement. Notations: Generic positive finite constants are denoted by K a , K b , . . ., and can take different values at different instances and are bounded in N and T (the panel dimensions). The largest and the smallest eigenvalues of the N × N real symmetric matrix A = (a ij ) are denoted by ζ max (A) and ζ min (A), respectively, and its spectral (or operator) norm by ∥A∥ = ζ 1/2 max (A ′ A). a.s. −→ denotes almost sure convergence, ℓ 1 −→ convergence in the ℓ 1 norm, p −→ convergence in probability, and d −→ convergence in distribution. We denote ∥x∥ 1 = ∑ n i=1 |x i | as the ℓ 1 norm of vector x. All asymptotics are carried out under N and T → ∞, jointly. Model and assumptionsWe consider a dynamic panel data model for i = 1, 2, . . . , N and t = 1, 2, . . . , T , where y it ∈ R is the response variable for cross-sectional unit i at time t and y it−1 denotes a lagged dependent variable. Consider the following conditional panel quantile function:The variable x it is a p x × 1 vector of regressors specific to cross-sectional unit i at time t,is a vector of latent factor loadings, and α i (τ ) is an individual effect potentially correlated with the regressor variables, x it . The term f ′ t γ i (τ ) can be interpreted as a quantile-specific function capturing unobserved heterogeneity that was not adequately controlled by the inclusion of x it .The model can be considered to be semi-parametric since the functional form of the conditionalis left unspecified and no parametric assumption is imposed on the relation between the regressors and the latent va...

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Empowering Consumers Through Data and Smart Technology: Experimental Evidence on the Consequences of Time‐of‐Use Electricity Pricing Policies

Cited by 49 publications

References 26 publications

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

Failing to Choose the Best Price: Theory, Evidence, and Policy

Common Correlated Effects Estimation of Heterogeneous: Dynamic Panel Quantile Regression Models

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