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on Discrete Choice Models |
| By: | Tu, Gengyang; Faure, Corinne; Schleich, Joachim; Guetlein, Marie-Charlotte |
| Abstract: | Smart thermostats may provide up to 10% savings in residential thermal energy use without loss of comfort, yet their diffusion has typically been slow. To better understand adoption of these devices, we conducted an online survey with approximately 5,500 respondents from eight European countries that included both a discrete choice experiment (DCE) and stated past adoption of smart thermostats. The results we obtained by estimating mixed logit models suggest that households value heating cost savings, remote temperature control, the display of changes in energy consumption, and recommendations by experts, albeit with substantial heterogeneity across countries; in comparison, subsidies are positively valued in all countries except for Germany and Spain, and recommendations by energy providers in all countries except Poland where they are negatively valued. Further, the findings provide evidence that consumer innovativeness reinforces the acceptance of technical attributes (heating cost savings, feedback functionalities, and remote temperature control), that privacy concerns reduce the acceptance of remote functionalities, and that stronger environmental identity reinforces the acceptance of environmentally related attributes (heating cost savings and feedback functionalities). The results we obtained from estimating binary response models of stated past adoption of smart thermostats are generally consistent with those of the DCE. |
| Keywords: | smart thermostats,smart home devices,choice experiment,mixed logit,innovativeness,privacy |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:fisisi:s202020&r=all |
| By: | Badruddoza, Syed (Washington State University); Amin, Modhurima (Washington State University); McCluskey, Jill (Washington State University) |
| Abstract: | Firms can prioritize among the product attributes based on consumer valuations using market-level data. However, a structural estimation of market demand is challenging, especially when the data are updating in real-time and instrumental variables are scarce. We find evidence that Random Forests (RF)—a machine-learning algorithm—can detect consumers’ sensitivity to product attributes similar to the structural framework of Berry-Levinsohn-Pakes (BLP). Sensitivity to an attribute is measured by the absolute value of its coefficient. We check the RF’s capacity to rank the attributes when prices are endogenous, coefficients are random, and instrumental or demographic variables are unavailable. In our simulations, the BLP estimates correlate with the RF importance factor in ranking (68%) and magnitude (79%), and the rates increase with the sample size. Consumer sensitivity to endogenous variables (price) and variables with random coefficients are overestimated by the RF approach, but ranking of variables with non-random coefficients match with BLP’s coefficients in 96% cases. These estimates are pessimistically derived by RF without parameter-tuning. We conclude that machine-learning does not replace the structural framework but provides firms with a sensible idea of consumers’ ranking of product attributes. |
| Keywords: | Machine-Learning; Random Forests; Demand Estimation; BLP; Discrete Choice. |
| JEL: | C55 D11 Q11 |
| Date: | 2019–12–04 |
| URL: | http://d.repec.org/n?u=RePEc:ris:wsuwpa:2019_005&r=all |
| By: | Philip Erickson |
| Abstract: | The conditional logit model is a standard workhorse approach to estimating customers' product feature preferences using choice data. Using these models at scale, however, can result in numerical imprecision and optimization failure due to a combination of large-valued covariates and the softmax probability function. Standard machine learning approaches alleviate these concerns by applying a normalization scheme to the matrix of covariates, scaling all values to sit within some interval (such as the unit simplex). While this type of normalization is innocuous when using models for prediction, it has the side effect of perturbing the estimated coefficients, which are necessary for researchers interested in inference. This paper shows that, for two common classes of normalizers, designated scaling and centered scaling, the data-generating non-scaled model parameters can be analytically recovered along with their asymptotic distributions. The paper also shows the numerical performance of the analytical results using an example of a scaling normalizer. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.08022&r=all |
| By: | Matthew A. Masten; Alexandre Poirier; Linqi Zhang |
| Abstract: | This paper provides a set of methods for quantifying the robustness of treatment effects estimated using the unconfoundedness assumption (also known as selection on observables or conditional independence). Specifically, we estimate and do inference on bounds on various treatment effect parameters, like the average treatment effect (ATE) and the average effect of treatment on the treated (ATT), under nonparametric relaxations of the unconfoundedness assumption indexed by a scalar sensitivity parameter c. These relaxations allow for limited selection on unobservables, depending on the value of c. For large enough c, these bounds equal the no assumptions bounds. Using a non-standard bootstrap method, we show how to construct confidence bands for these bound functions which are uniform over all values of c. We illustrate these methods with an empirical application to effects of the National Supported Work Demonstration program. We implement these methods in a companion Stata module for easy use in practice. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.15716&r=all |
| By: | Shuhei Nishitateno (School of Policy Studies, Kwansei Gakuin University); Paul J. Burke (Crawford School of Public Policy, Australian National University) |
| Abstract: | This paper documents the effect of diesel vehicle registration restrictions introduced in Japan in 2001 in reducing suspended particulate matter (SPM) concentrations. The focus is on Aichi and Mie prefectures, home to a number of municipalities that were required to implement these restrictions in 2001. The paper then uses this intervention to estimate the causal effect of air pollution on land values. We obtain estimates of the elasticity of residential land prices with respect to SPM concentration of between –0.4 and –1.0. The revealed willingness to pay for the improvements in air quality induced by the intervention in Aichi and Mie is estimated at about US$7 billion. We also find evidence that net in-migration appears to be a key mechanism via which clean air was capitalized into higher land values. The results are robust to a number of estimation approaches and sample restrictions. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:een:ccepwp:2101&r=all |
| By: | Bora Kim |
| Abstract: | Randomized experiments have become a standard tool in economics. In analyzing randomized experiments, the traditional approach has been based on the Stable Unit Treatment Value (SUTVA: \cite{rubin}) assumption which dictates that there is no interference between individuals. However, the SUTVA assumption fails to hold in many applications due to social interaction, general equilibrium, and/or externality effects. While much progress has been made in relaxing the SUTVA assumption, most of this literature has only considered a setting with perfect compliance to treatment assignment. In practice, however, noncompliance occurs frequently where the actual treatment receipt is different from the assignment to the treatment. In this paper, we study causal effects in randomized experiments with network interference and noncompliance. Spillovers are allowed to occur at both treatment choice stage and outcome realization stage. In particular, we explicitly model treatment choices of agents as a binary game of incomplete information where resulting equilibrium treatment choice probabilities affect outcomes of interest. Outcomes are further characterized by a random coefficient model to allow for general unobserved heterogeneity in the causal effects. After defining our causal parameters of interest, we propose a simple control function estimator and derive its asymptotic properties under large-network asymptotics. We apply our methods to the randomized subsidy program of \cite{dupas} where we find evidence of spillover effects on both short-run and long-run adoption of insecticide-treated bed nets. Finally, we illustrate the usefulness of our methods by analyzing the impact of counterfactual subsidy policies. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.13710&r=all |
| By: | Chie Aoyagi; Alistair Munro |
| Abstract: | The quantification of how aspects of a job are valued by employees sheds light on the potential for labor market reform in Japan. Using a nationwide sample of 1,046 working-age adults, we conduct a choice experiment that examines individuals’ willingness to trade wages against job characteristics such as the extent of overtime, job security, the possibility of work transfer and relocation. Our results suggest that: i) workers have high WTP (willingness to pay) to avoid extreme overtime and work transfer, ii) women have higher WTP than men, and iii) higher WTP for women are driven in part by feelings of guilt. |
| Keywords: | Wages;Women;Labor;Education;Gender;WP,gender wage gap,wage difference,job characteristic |
| Date: | 2019–11–27 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2019/261&r=all |
| By: | Shakeeb Khan; Arnaud Maurel; Yichong Zhang |
| Abstract: | We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a non-standard exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor. |
| JEL: | C14 C21 C25 C38 |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28327&r=all |