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on Discrete Choice Models |
| By: | Goos P.; Vermeulen B.; Vandebroek M. |
| Abstract: | Despite the fact that many conjoint choice experiments offer respondents a no-choice option in every choice set, the optimal design of conjoint choice experiments involving no-choice options has received only a limited amount of attention in the literature. In this article, we present an approach to construct D-optimal designs for this type of experiment. For that purpose, we derive the information matrix of a nested multinomial logit model that is appropriate for analyzing data from choice experiments with no-choice options. The newly derived information matrix is compared to the information matrix for the multinomial logit model that is used in the literature to construct designs for choice experiments. It is also used to quantify the loss of information in a choice experiment due to the presence of a no-choice option. |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:ant:wpaper:2008020&r=dcm |
| By: | James J. Heckman; Sergio Urzua; Edward Vytlacil |
| Abstract: | This paper develops the method of local instrumental variables for mod- els with multiple, unordered treatments when treatment choice is determined by a nonparametric version of the multinomial choice model. Responses to interventions are permitted to be heterogeneous in a general way and agents are allowed to select a treatment (e.g. participate in a program) with at least partial knowledge of the idiosyncratic response to the treatments. We define treatment effects in a general model with multiple treatments as differences in counterfactual outcomes that would have been observed if the agent faced different choice sets. We show how versions of local instrumental variables can identify the corresponding treatment parameters. Direct application of local instrumental variables identies the marginal treatment effect of one option versus the next best alternative without requiring knowledge of any structural parameters from the choice equation or any large support assumptions. Using local instrumental variables to identify other treatment parameters requires ei- ther large support assumptions or knowledge of the latent index function of the multinomial choice model. |
| Date: | 2008–12–15 |
| URL: | http://d.repec.org/n?u=RePEc:ucd:wpaper:200830&r=dcm |
| By: | Andrén, D; Andrén, T |
| Abstract: | This paper analyzes the effects of being on part-time sick leave compared to full-time sick leave on the probability of recovering (i.e., returning to work with full recovery of lost work capacity). Using a discrete choice one-factor model, we estimate mean treatment parameters and distributional treatment parameters from a common set of structural parameters. Our results show that part-time sick leave increases the likelihood of recovering and dominates full-time sick leave for sickness spells of 150 days or longer. For these long spells, the probability of recovering increases by 10 percentage points. |
| Keywords: | part-time sick leave, selection, unobserved heterogeneity, treatment effects |
| JEL: | I12 J21 J28 |
| Date: | 2009–01 |
| URL: | http://d.repec.org/n?u=RePEc:yor:hectdg:09/01&r=dcm |
| By: | Luis Fernando Gamboa; Nohora Forero Ramírez |
| Abstract: | We analyze the Body Mass Index (BMI) in a distinct way of its traditional use and it lets us use it as a proxy of standard of living for the case of Colombia. Our approach is focused on studying how far the people are from the normal range and not on the score of each one and this lets us to treat equally extreme cases as severe thinness and obesity. We use a probabilistic model (Ordered Probit) that evaluates the probability of being within the normal range or another level. We found that socioeconomic variables have a significant effect on the dependent variable and that there are no linear effects. Besides, people with difficulties for walking and adults have less probability of having a normal BMI. |
| Date: | 2009–01–08 |
| URL: | http://d.repec.org/n?u=RePEc:col:000092:005218&r=dcm |