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on Discrete Choice Models |
| By: | Daziano, Ricardo A.; Achtnicht, Martin |
| Abstract: | Previous literature on the distribution of willingness to pay has focused on its heterogeneity distribution without addressing exact interval estimation. In this paper we derive and analyze Bayesian confidence sets for quantifying uncertainty in the determination of willingness to pay for carbon dioxide abatement. We use two empirical case studies: household decisions of energy-efficient heating versus insulation, and purchase decisions of ultralow-emission vehicles. We first show that deriving credible sets using the posterior distribution of the willingness to pay is straightforward in the case of deterministic consumer heterogeneity. However, when using individual estimates, which is the case for the random parameters of the mixed logit model, it is complex to define the distribution of interest for the interval estimation problem. This latter problem is actually more involved than determining the moments of the heterogeneity distribution of the willingness to pay using frequentist econometrics. A solution that we propose is to derive and then summarize the distribution of point estimates of the individual willingness to pay under different loss functions. -- |
| Keywords: | Discrete Choice Models,Willingness to Pay,Credible Sets |
| JEL: | C25 D12 Q51 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:13059&r=dcm |
| By: | Tomás del Barrio Casto (University of the Balearic Islands, Palma de Mallorca); William Nilsson (University of the Balearic Islands, Palma de Mallorca); Andrés J. Picazo-Tadeo (University of Valencia) |
| Abstract: | Discrete choice models such as the conditional logit model are widely used tools in applied econometrics and, particularly, in the eld of environmental valuation and welfare measurement in order to provide policymakers with sound information for making strategic choices. Monte Carlo simulations are used in this study to analyze biases due to omitted relevant variables and functional form misspeci cation in the conditional logit model. Using an easy-to-estimate speci cation test is effective to reduce the risks for large biases. One somewhat discouraging result is, however, that a moderate bias can be found even when the omitted variable is orthogonal to the explanatory variables included. This result is particularly interesting considering the increasing interest in using randomized experiments to obtain causal interpretations of key parameters. Randomization, with independence between included and omitted variables, does not guarantee unbiased estimates in the conditional logit model. |
| Keywords: | Environmental valuation, Welfare measurements, Choice experiments, Monte Carlo analysis, Speci cation tests |
| JEL: | C51 D69 C99 C15 |
| Date: | 2013–07 |
| URL: | http://d.repec.org/n?u=RePEc:eec:wpaper:1318&r=dcm |
| By: | Anna Bartczak (Faculty of Economic Sciences, University of Warsaw; Warsaw Ecological Economics Center) |
| Abstract: | The purpose of this study is to investigate the impact of an individual trait of altruism on social preferences and hence willingness to pay (WTP) for changes in forest management strategies in the Białowieża Forest in Poland. We used data from a discrete choice experiment (CE), where attributes described changes in the quality of the forest and recreation and were framed to capture the respondents’ non-use and use motivations. Patterns in the individual differences in altruistic behavior were elicited using a self-reported questionnaire developed by Rushton et al. (1981) concerning the frequency of an engagement in different altruistic behaviors. The application of the choice experiment technique allowed for the disentangling of the effect of a trait of altruism with regard to different attributes and their levels. The parameterization we employed in the survey was a WTP-space model (Train and Weeks 2005). Results show that the level of altruism has a significant effect on the valuation of restrictions in the forest visitor numbers; however, the altruism influence on the existence and bequest value from improving nature preservation depends on the current status of the forest. |
| Keywords: | altruism, Choice Experiment, forest naturalness, number of visitors, use and non-use value, WTP-space model |
| JEL: | Q23 Q51 Q56 Q57 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2013-20&r=dcm |
| By: | Andrew Chesher (Institute for Fiscal Studies and University College London); Adam Rosen (Institute for Fiscal Studies and University College London) |
| Abstract: | The ability to allow for flexible forms of unobserved heterogeneity is an essential ingredient in modern microeconometrics. In this paper we extend the application of instrumental variable (IV) models to a wide class of problems in which multiple values of unobservable variables can be associated with particular combinations of observed endogenous and exogenous variables. In our Generalised Instrumental Variable (GIV) models, in contrast to traditional IV models, the mapping from unobserved heterogeneity to endogenous variables need not admit a unique inverse. The class of GIV models allows unobservables to be multivariate and to enter nonseparably into the determination of endogenous variables, thereby removing strong practical limitations on the role of unobserved heterogeneity. Important examples include models with discrete or mixed continuous/discrete outcomes and continuous unobservables, and models with excess heterogeneity where many combinations of different values of multiple unobserved variables, such as random coefficients, can deliver the same realisations of outcomes. We use tools from random set theory to study identification in such models and provide a sharp characterisation of the identified set of structures admitted. We demonstrate the application of our analysis to a continuous outcome model with an interval-censored endogenous explanatory variable. |
| Keywords: | instrumental variables, endogeneity, excess heterogeneity, limited information, set identification, partial identification, random sets, incomplete models |
| JEL: | C10 C14 C24 C26 |
| Date: | 2013–08 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:cemmap:43/13&r=dcm |
| By: | Daniel Kemptner |
| Abstract: | This paper proposes a dynamic life cycle model of health risks, employment, early retirement, and wealth accumulation in order to analyze the health-related risks of consumption and old age poverty. In particular, the model includes a health process, the interaction between health and employment risks, and an explicit modeling of the German public insurance schemes. I rely on a dynamic programming discrete choice framework and estimate the model using data from the German Socio-Economic Panel. I quantify the health-related life cycle risks by simulating scenarios where health shocks do or do not occur at different points in the life cycle for individuals with differing endowments. Moreover, a policy simulation investigates minimum pension benefits as an insurance against old age poverty. While such a reform raises a concern about an increase in abuse of the early retirement option, the simulations indicate that a means test mitigates<br /> the moral hazard problem substantially. |
| Keywords: | dynamic programming, discrete choice, health, employment, early retirement, consumption, tax and transfer system |
| JEL: | C61 I14 J22 J26 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwsop:diw_sp583&r=dcm |