nep-dcm 2014-04-05 papers

on Discrete Choice Models

Issue of 2014‒04‒05
two papers chosen by
Edoardo Marcucci
Universita' di Roma Tre

Fictive Learning in Choice under Uncertainty: A Logistic Regression Model By Donald J. Brown; Oliver Bunn; Caterina Calsamiglia; Donald J. Brown
A Note on the Quantile Formulation By Zuo Quan Xu

Fictive Learning in Choice under Uncertainty: A Logistic Regression Model

By:	Donald J. Brown (Dept. of Economics, Yale University); Oliver Bunn (Dept. of Economics, Yale University); Caterina Calsamiglia; Donald J. Brown
Abstract:	This paper is an exposition of an experiment on revealed preferences, where we posit a novel discrete binary choice model. To estimate this model, we use general estimating equations or GEE. This is a methodology originating in biostatistics for estimating regression models with correlated data. In this paper, we focus on the motivation for our approach, the logic and intuition underlying our analysis and a summary of our findings. The missing technical details are in the working paper by Bunn et al. (2013). The experimental data is available from the corresponding author: donald.brown@yale.edu. The recruiting poster and informed consent form are attached as appendices.
Keywords:	Counterfactual outcomes, Odds ratios, Alternating logistic regression
JEL:	C23 C35 C91 D03
Date:	2013–03
URL:	http://d.repec.org/n?u=RePEc:cwl:cwldpp:1890r&r=dcm

A Note on the Quantile Formulation

By:	Zuo Quan Xu
Abstract:	Many investment models in discrete or continuous-time settings boil down to maximizing an objective of the quantile function of the decision variable. This quantile optimization problem is known as the quantile formulation of the original investment problem. Under certain monotonicity assumptions, several schemes to solve such quantile optimization problems have been proposed in the literature. In this paper, we propose a change-of-variable and relaxation method to solve the quantile optimization problems without using the calculus of variations or making any monotonicity assumptions. The method is demonstrated through a portfolio choice problem under rank-dependent utility theory (RDUT). We show that solving a portfolio choice problem under RDUT reduces to solving a classical Merton's portfolio choice problem under expected utility theory with the same utility function but a different pricing kernel explicitly determined by the given pricing kernel and probability weighting function. With this result, the feasibility, well-posedness, attainability and uniqueness issues for the portfolio choice problem under RDUT are solved. The method is applicable to general models with law-invariant preference measures including portfolio choice models under cumulative prospect theory (CPT) or RDUT, Yaari's dual model, Lopes' SP/A model, and optimal stopping models under CPT or RDUT.
Date:	2014–03
URL:	http://d.repec.org/n?u=RePEc:arx:papers:1403.7269&r=dcm

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