|
on Discrete Choice Models |
| By: | Jonathan James (Department of Economics, California Polytechnic State University) |
| Abstract: | Mixed logit models with normally distributed random coefficients are typically estimated under the extreme assumptions that either the random coefficients are completely independent or fully correlated. A factor structured covariance provides a middle ground between these two assumptions. However, because these models are more difficult to estimate, they are not frequently used to model preference heterogeneity. This paper develops a simple expectation maximization algorithm for estimating mixed logit models when preferences are generated from a factor structured covariance. The algorithm is easy to implement for both exploratory and confirmatory factor models. The estimator is applied to stated-preference survey data from residential energy customers (Train, 2007). Comparing the fit across five different models, which differed in their assumptions on the covariance of preferences, the results show that all three factor specifications produced a better fit of the data than the fully correlated model measured by BIC and two out of three performed better in terms of AIC. |
| Keywords: | Discrete Choice, Mixed Logit, EM Algorithm, Factor Models |
| JEL: | C02 C13 C25 C35 C38 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:cpl:wpaper:1802&r=dcm |
| By: | Akshay Vij; Rico Krueger |
| Abstract: | This study proposes a mixed logit model with multivariate nonparametric finite mixture distributions. The support of the distribution is specified as a high-dimensional grid over the coefficient space, with equal or unequal intervals between successive points along the same dimension; the location of each point on the grid and the probability mass at that point are model parameters that need to be estimated. The framework does not require the analyst to specify the shape of the distribution prior to model estimation, but can approximate any multivariate probability distribution function to any arbitrary degree of accuracy. The grid with unequal intervals, in particular, offers greater flexibility than existing multivariate nonparametric specifications, while requiring the estimation of a small number of additional parameters. An expectation maximization algorithm is developed for the estimation of these models. Multiple synthetic datasets and a case study on travel mode choice behavior are used to demonstrate the value of the model framework and estimation algorithm. Compared to extant models that incorporate random taste heterogeneity through continuous mixture distributions, the proposed model provides better out-of-sample predictive ability. Findings reveal significant differences in willingness to pay measures between the proposed model and extant specifications. The case study further demonstrates the ability of the proposed model to endogenously recover patterns of attribute non-attendance and choice set formation. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1802.02299&r=dcm |
| By: | Paramita Sinha; Martha L. Caulkins; Maureen L. Cropper |
| Abstract: | Amenities that vary across cities are typically valued using either a hedonic model, in which amenities are capitalized into wages and housing prices, or a discrete model of household location choice. In this paper, we use the 2000 Public Use Microdata Sample (PUMS) to value climate amenities using both methods. We compare estimates of marginal willingness to pay (MWTP), first assuming homogeneous tastes for climate amenities and then allowing preferences for climate amenities to vary by location. We find that mean MWTP for warmer winters is about four times larger using the discrete choice approach than with the hedonic approach; mean MWTP for cooler summers is twice as large. The two approaches also differ in their estimates of taste sorting. The discrete choice model implies that households with the highest MWTP for warmer winters locate in cities with the mildest winters, while the hedonic model does not. Differences in estimates are due to three factors: (1) the discrete choice model incorporates the psychological costs of moving from one’s birthplace, which the hedonic models do not; (2) the discrete choice model allows for city-specific labor and housing markets, rather than assuming a national market; (3) the discrete choice model uses information on market shares (i.e., population) in estimating parameters, which the hedonic model does not. |
| JEL: | Q51 Q54 |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24290&r=dcm |
| By: | Simora, Michael |
| Abstract: | Despite general support for the transition towards renewable energies, local opposition may hamper the required power line construction. This paper evaluates a large randomized one shot binary choice experiment with about 10,000 observations to examine the effect of annual community compensations based on current legislation as well as the effect of household compensations on the willingness to accept new power line construction. Results reveal that community compensations have no bearing on the acceptance level, whereas personal compensations have a negative effect via crowding out intrinsic motivation to support the construction project or via signaling negative impacts for residents. Thus, policy makers should refrain from financial payments as an instrument to decrease local opposition. |
| Keywords: | not-in-my-backyard,compensation payment,willingness to accept,motivation crowding out |
| JEL: | M52 C93 Q40 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:729&r=dcm |
| By: | Feucht, Yvonne; Zander, Katrin |
| Abstract: | The purchase of products labelled with Carbon footprints is one option for consumers to act climate-friendly and consumers frequently state that they are interested in this kind of labels. But even though various carbon footprint labelling schemes exist throughout Europe, their market relevance is low. In this context, the present research investigates preferences for climate-friendly food and identifies barriers for climate friendly food choices in the European market. Using a mixed methods approach combining an online survey (choice experiments and a questionnaire) with qualitative face-to-face interviews, the preferences and willingness to pay for different carbon labels and a climate-friendly claim were explored in six European countries. While the online survey mainly aimed at eliciting consumer preferences for different ways of communicating climate-friendliness, the face-to-face interviews which were based on the results of the online survey, deepened and broadened the quantitative results. Thereby, consumers' perceptions of climate-friendly food and their information needs with respect to climate-friendly food are elicited. Our results show that the presence of a carbon label on a product increases the purchase probability and that consumers are willing to pay a (small) price premium for a carbon label in all countries under investigation (France, Germany, Italy, Norway, Spain, Germany, UK). However, the contribution of a carbon label to a more climate-friendly consumption will be limited. Main reasons are the lack of knowledge of climate friendly actions, reluctance to change consumption habits (e.g. meat and dairy consumption), time preference and uncertainty regarding the relevance of climate change. Consumers appear to be frequently overstrained with respect to climate-friendly buying decisions. Policy makers and retailers are challenged to set appropriate structures to support climate-friendly consumption. |
| Keywords: | carbon footprint labelling,consumer research,climate change,climate-friendly food,mixed methods,choice experiments,CO2-Labels,Verbraucherforschung,Klimawandel,Klimafreundliche Lebensmittel,Mixed methods,Kaufexperimente |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:jhtiwp:78&r=dcm |
| By: | Songnian Chen (HKUST); Shakeeb Khan (Boston College); Xun Tang (Rice University) |
| Abstract: | In this note we revisit the use of exclusion restrictions in the semiparametric binary choice panel data model introduced in Honore and Lewbel (2002). We show that in a dynamic panel data setting (where one of the pre-determined explanatory variables is the lagged dependent variable), the exclusion restriction in Honore and Lewbel (2002) implicitly re- quires serial independence condition on an observed regressor, that if violated in the data will result in their procedure being inconsistent. We propose a new identification strategy and estimation procedure for the semiparametric binary panel data model under exclusion restrictions that accommodate the serial correlation of observed regressors in a dynamic setting. The new estimator converges at the parametric rate to a limiting normal distri- bution. This rate is faster than the nonparametric rates of existing alternative estimators for the binary choice panel data model, including the static case in Manski (1987) and the dynamic case in Honore and Kyriazidou (2000). |
| Keywords: | Panel Data, Dynamic Binary Choice, Exclusion Restriction |
| JEL: | C14 C23 C25 |
| Date: | 2018–02–12 |
| URL: | http://d.repec.org/n?u=RePEc:boc:bocoec:947&r=dcm |
| By: | Abhijit Banerjee; Dean Karlan; Robert Darko Osei; Hannah Trachtman; Christopher Udry |
| Abstract: | A multi-faceted program comprising a grant of productive assets, training, coaching, and savings has been found to build sustainable income for those in extreme poverty. We focus on two important questions: whether a mere grant of productive assets would generate similar impacts (it does not), and whether access to a savings account and a deposit collection service would generate similar impacts (it does not). |
| JEL: | D12 O12 O17 |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24271&r=dcm |