|
on Discrete Choice Models |
| By: | Thomas J. Steenburgh (Harvard Business School, Marketing Unit); Andrew Ainslie (UCLA Anderson, School of Management) |
| Abstract: | Previous research suggests that the random coefficients logit is a highly flexible model that overcomes the problems of the homogeneous logit by allowing for differences in tastes across individuals. The purpose of this paper is to show that this is not true. We prove that the random coefficients logit imposes restrictions on individual choice behavior that limit the types of substitution patterns that can be found through empirical analysis, and we raise fundamental questions about when the model can be used to recover individuals' preferences from their observed choices. Part of the misunderstanding about the random coefficients logit can be attributed to the lack of cross-level inference in previous research. To overcome this deficiency, we design several Monte Carlo experiments to show what the model predicts at both the individual and the population levels. These experiments show that the random coefficients logit leads a researcher to very different conclusions about individuals' tastes depending on how alternatives are presented in the choice set. In turn, these biased parameter estimates affect counterfactual predictions. In one experiment, the market share predictions for a given alternative in a given choice set range between 17% and 83% depending on how the alternatives are displayed both in the data used for estimation and in the counterfactual scenario under consideration. This occurs even though the market shares observed in the data are always about 50% regardless of the display. |
| Date: | 2010–01 |
| URL: | http://d.repec.org/n?u=RePEc:hbs:wpaper:10-053&r=dcm |
| By: | Pulina, Manuela; Paba, Antonello |
| Abstract: | This paper analyses the demographic, socio-economics and banking specific determinants that influence the risk of fraud in a portfolio of credit cards. The data are from recent account archives for cards issued throughout Italy. A logit framework is employed that incorporates cards at a risk of fraud as the dependent variable and a set of explanatory variables (e.g. gender, location, credit line, number of transactions in euros and in non euros currency). The empirical results provide useful indicators on the factors that are responsible for potential risk of fraud. |
| Keywords: | credit card; fraud; demographic and socio-economics factors; logit modelling. |
| JEL: | D12 C35 G21 |
| Date: | 2010–04 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:20019&r=dcm |
| By: | Breitmoser, Yves |
| Abstract: | This paper analyzes strategic choice in p-beauty contests. We first show that it is not generally a best reply to guess the expected target value (accounting for the own weight) even in games with n>2 players and that iterated best response sequences are not unique even after perfect/cautious refinement. This implies that standard formulations of ``level-k'' models are neither exactly nor uniquely rationalizable by belief systems based on iterated best response. Second, exact modeling of iterated reasoning weakens the fit considerably and reveals that equilibrium types dominate the populations. We also show that ``levels of reasoning'' cannot be measured regardless of the underlying model. Third, we consider a ``nested logit'' model where players choose their level. It dispenses with belief systems between players and is rationalized by a random utility model. Besides being internally consistent, nested logit equilibrium fits better than three variants of the level-k model in standard data sets. |
| Keywords: | logit equilibrium; hierarchical response; level-k; beauty contest |
| JEL: | C44 C72 |
| Date: | 2010–01–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:19893&r=dcm |