We study the identification and estimation of structural parameters in dynamic panel data logit models where decisions are forward-looking joint distribution unobserved heterogeneity observable state variables is nonparametric, i.e., fixed-effects model. consider with two endogenous variables: lagged decision variable, time duration last choice. This class includes as particular cases important...

This paper studies posterior contraction rates in multi-category logit models with priors incorporating group sparse structures. We consider a general class of that includes the well-known multinomial as special case. Group sparsity is useful when predictor variables are naturally clustered and particularly for variable selection models. provide unified platform group-sparse include binary logi...

This paper proposes a discrete-time hazard regression approach based on the relation between hazard rate models and excess over threshold models, which are frequently encountered in extreme value modelling. The proposed duration model employs a exible link function and incorporates the grouped-duration analogue of the wellknown Cox proportional hazards model and the proportional odds model as s...

A growing body of empirical literature uses structurally-derived economic models to study the nature of competition and to measure explicitly the economic impact of strategic policies. While several approaches have been proposed, the discrete choice demand system has experienced wide usage. The heterogeneous, or ‘‘mixed’’, logit in particular has been widely applied due to its parsimonious stru...

This talk considers the special case of binomial data where repeated measurements are available for identical covariate patterns. Our method is based on various Bayesian approaches for estimating binary logit models. To carry out MCMC sampling with data augmentation, logit models are rewritten as random utility models (RUM) or difference RUM (dRUM). Following earlier papers (Frühwirth-Schnatter...

An exact formula for the expected compensating variation is derived for logit and nested-logit models with income effects. Intuition, examples, and an application are provided. The appendix contains a formal proof. The formula is applied to estimate the E[cv]s salmon anglers in Maine would associate with changes in catch rates at Maine and Canadian Rivers.

This article introduces a Markov chain Monte Carlo (MCMC) method for sampling the parameters of a multinomial logit model from their posterior distribution. Let yi ∈ {0, . . . ,M} denote the categorical response of subject i with covariates xi = (xi1, . . . , xip) T . Let X = (x1, . . . ,xn) T denote the design matrix, and let y = (y1, . . . , yn) T . Multinomial logit models relate yi to xi th...

1 Mixture Model and Bayesian Model Interpretations Combining Equations 2.1 and 2.2 (Main Text), we can consider the empirical-Bayes hierarchical model as a mixed model with both fixed and random effects in the frequentist setting as given by: logit(pmi) = αm + ZmγXmi +Xmiτ tmm, where ZmγXmi would be considered a fixed effect and Xmiτ tmm a random effect. Alternatively, this model may be viewed ...

The assortment optimization problem under the mixture of multinomial logit models is NPcomplete and there are different approximation methods to obtain upper bounds on the optimal expected revenue. In this paper, we analytically compare the upper bounds obtained by the different approximation methods. We propose a new, tractable approach to construct an upper bound on the optimal expected reven...