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on Discrete Choice Models |
| By: | Fosgerau, Mogens; Ranjan, Abhishek |
| Abstract: | This note establishes a new identification result for additive random utility discrete choice models (ARUM). A decision-maker associates a random utility U_{j}+m_{j} to each alternative in a finite set j∈{1,...,J}, where U={U₁,...,U_{J}} is unobserved by the researcher and random with an unknown joint distribution, while the perturbation m=(m₁,...,m_{J}) is observed. The decision-maker chooses the alternative that yields the maximum random utility, which leads to a choice probability system m→(Pr(1|m),...,Pr(J|m)). Previous research has shown that the choice probability system is identified from the observation of the relationship m→Pr(1|m). We show that the complete choice probability system is identified from observation of a relationship m→∑_{j=1}^{s}Pr(j|m), for any s |
| Keywords: | ARUM; random utility discrete choice; identification |
| JEL: | C25 D11 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:76800&r=dcm |
| By: | Bart Smeulders; Clintin Davis-Stober; Michel Regenwetter; Frits Spieksma |
| Abstract: | In so-called random preference models of probabilistic choice, a decision maker chooses according to an unspecified probability distribution over preference states. The most prominent case arises when preference states are linear orders or weak orders of the choice alternatives. The literature has documented that actually evaluating whether decision makers' observed choices are consistent with such a probabilistic model of choice poses computational difficulties. This severely limits the possible scale of empirical work in behavioral economics and related disciplines. We propose a family of column generation based algorithms for performing such tests. We evaluate our algorithms on various sets of instances. We observe substantial improvements in computation time and conclude that we can efficiently test substantially larger data sets than previously possible. |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:ete:kbiper:572504&r=dcm |
| By: | Biyase, Mduduzi; Zwane, Talent |
| Abstract: | The data used for our analysis is drawn from the first four waves of the National Income Dynamic Study to determine the factors that influence poverty and household welfare in South Africa. Contrary to most existing studies, which have applied ordinary least squares and probit/logit models on cross-sectional data, this analysis captures unobserved individual heterogeneity and endogeneity, both via fixed effect, and via a robust alternative based on random effect probit estimation. The results from fixed effect and random effect probit indicate that levels of education of the household head, some province dummies, race of the household head, dependency ratio, gender of the household head, employment status of the household head and marital status of the household head are statistically significant determinants of household welfare. Consistent with previous research, we also found that, compared to traditional rural areas (used as reference category), households living in urban and farms are less likely to be poverty stricken, which implies that rural areas (traditional rural areas) should continue to be a major focus of poverty alleviation efforts in South Africa. |
| Keywords: | fixed effect, random effect probit, poverty |
| JEL: | I31 |
| Date: | 2017–01–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:77085&r=dcm |