|
on Discrete Choice Models |
| By: | Youssef M. Aboutaleb; Mazen Danaf; Yifei Xie; Moshe Ben-Akiva |
| Abstract: | This paper discusses capabilities that are essential to models applied in policy analysis settings and the limitations of direct applications of off-the-shelf machine learning methodologies to such settings. Traditional econometric methodologies for building discrete choice models for policy analysis involve combining data with modeling assumptions guided by subject-matter considerations. Such considerations are typically most useful in specifying the systematic component of random utility discrete choice models but are typically of limited aid in determining the form of the random component. We identify an area where machine learning paradigms can be leveraged, namely in specifying and systematically selecting the best specification of the random component of the utility equations. We review two recent novel applications where mixed-integer optimization and cross-validation are used to algorithmically select optimal specifications for the random utility components of nested logit and logit mixture models subject to interpretability constraints. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2101.10261&r=all |
| By: | Georges Sfeir; Filipe Rodrigues; Maya Abou-Zeid |
| Abstract: | We present a Gaussian Process - Latent Class Choice Model (GP-LCCM) to integrate a non-parametric class of probabilistic machine learning within discrete choice models (DCMs). Gaussian Processes (GPs) are kernel-based algorithms that incorporate expert knowledge by assuming priors over latent functions rather than priors over parameters, which makes them more flexible in addressing nonlinear problems. By integrating a Gaussian Process within a LCCM structure, we aim at improving discrete representations of unobserved heterogeneity. The proposed model would assign individuals probabilistically to behaviorally homogeneous clusters (latent classes) using GPs and simultaneously estimate class-specific choice models by relying on random utility models. Furthermore, we derive and implement an Expectation-Maximization (EM) algorithm to jointly estimate/infer the hyperparameters of the GP kernel function and the class-specific choice parameters by relying on a Laplace approximation and gradient-based numerical optimization methods, respectively. The model is tested on two different mode choice applications and compared against different LCCM benchmarks. Results show that GP-LCCM allows for a more complex and flexible representation of heterogeneity and improves both in-sample fit and out-of-sample predictive power. Moreover, behavioral and economic interpretability is maintained at the class-specific choice model level while local interpretation of the latent classes can still be achieved, although the non-parametric characteristic of GPs lessens the transparency of the model. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2101.12252&r=all |
| By: | Klöble, Katrin |
| Abstract: | This paper addresses the self-selection of potential migrants. In particular, the study examines whether risk and time preferences explain a significant proportion in the movement heterogeneity of individuals. It is further intended to shed light on the role of social preferences (trust, altruism, reciprocity) as potential migratory determinants. By making use of a unique cross-sectional data set on migration intentions (Gallup World Poll) and experimentally-validated preferences (the Global Preference Survey) covering 70 countries worldwide, a probit model is estimated. The empirical results provide evidence that potential migrants exhibit higher levels of risk-taking and patience than their counterparts who stay at home (the stayers). This holds true across differing countries with various cultural backgrounds and income levels. Trust and negative reciprocity are found to be significantly related to migration aspirations as well. Yet conclusive clarifications still remain necessary, providing impetuses for future research. |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:diedps:42021&r=all |