|
on Discrete Choice Models |
| By: | Jeremy T. Fox |
| Abstract: | I prove that the joint distribution of random coefficients and additive errors is identified in a mulltinomial choice model. No restrictions are imposed on the support of the random coefficients and additive errors. The proof uses large support variation in choice-specific explanatory variables following Lewbel (2000) but does not rely on an identification at infinity technique where the payoffs of all but two choices are set to minus infinity. |
| JEL: | C25 L0 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:23621&r=dcm |
| By: | Fabian Eckert (Yale University); Costas Arkolakis (Yale University) |
| Abstract: | Combinatorial problems are prevalent in economics but the large dimensionality of potential solutions substantially limits the scope of their applications. We define and characterize a general class that we term combinatorial discrete choice problems and show that it incorporates existing problems in economics and engineering. We prove that intuitive sufficient conditions guarantee the existence of simple recursive procedures that can be used to identify the global maximum. We propose such an algorithm and show how it can be used to revisit problems whose computation was deemed infeasible before. We finally discuss results for a class of games characterized by these sufficient conditions. |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed017:249&r=dcm |
| By: | Martine AUDIBERT (FERDI); Yong HE (FERDI); Jacky MATHONNAT (Cerdi - University of Auvergne) |
| Abstract: | The objective of this paper is twofold: 1) to fill the gap in the health care literature with the estimation of the price and distance effects on health care provider choices by households in the presence of varying demand heterogeneity, 2) to contribute to estimation robustness by confronting the performance of the mixed multinomial logit (MMNL) and the multinomial logit (MNL). We built a database of two samples of patients surveyed within the same regions in rural China over a time interval of 18 years, and presumed varying demand heterogeneity due to income increase and people aging. We find that while the mean price and distance negative effects on patients choice were present in both time periods, their differences in heterogeneity, which were confirmed with the MMNL, could have crucial importance in avoiding erroneous policy making based merely on mean price and distance effects. We also find that while both the MNL and the MMNL are able to predict price and distance effects with low heterogeneity, only the MMNL appears able to detect the price effect when heterogeneity is high. This finding has policy implications and suggests using caution when interpreting estimation results with the MNL in cases of high heterogeneity. |
| Keywords: | price effect, distance effect, health care choice, preference heterogeneity, multinomial and mixed logit model, estimation robustness, Chinese rural households |
| JEL: | D1 C5 I1 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:fdi:wpaper:3872&r=dcm |
| By: | Martine AUDIBERT (FERDI); Yong HE (FERDI); Jacky MATHONNAT (Cerdi - Université d'Auvergne) |
| Abstract: | The objective of this paper is twofold: 1) to fill the gap in the health care literature with the estimation of the price and distance effects on health care provider choices by households in the presence of varying demand heterogeneity, 2) to contribute to estimation robustness by confronting the performance of the mixed multinomial logit (MMNL) and the multinomial logit (MNL). We built a database of two samples of patients surveyed within the same regions in rural China over a time interval of 18 years, and presumed varying demand heterogeneity due to income increase and people aging. We find that while the mean price and distance negative effects on patients choice were present in both time periods, their differences in heterogeneity, which were confirmed with the MMNL, could have crucial importance in avoiding erroneous policy making based merely on mean price and distance effects. We also find that while both the MNL and the MMNL are able to predict price and distance effects with low heterogeneity, only the MMNL appears able to detect the price effect when heterogeneity is high. This finding has policy implications and suggests using caution when interpreting estimation results with the MNL in cases of high heterogeneity. |
| Keywords: | price effect, distance effect, health care choice, preference heterogeneity, multinomial and mixed logit model, estimation robustness, Chinese rural households |
| JEL: | D1 C5 I1 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:fdi:wpaper:3871&r=dcm |