In a recent paper in this journal, Wojcik (2000) argues that the nested logit model “is likely to be superior” to alternative random coefficients logit specifications, like those we and many others have used in recent work (e.g. Berry, Levinsohn, and Pakes (1995), henceforth BLP). Her conclusion is based on a within sample “prediction” exercise. We would like to raise several issues about her c...

We develop a method for estimating the rivalness of tax bases using the structures of the conditional logit, Poisson and nested logit models. As an illustration, we apply this method to estimate the e↵ect of state-level capital taxation on U.S. inward foreign direct investment. The assumption of perfect non-rivalenss can in some cases be rejected, but the assumption of perfect rivalness cannot....

This paper develops a new technique for estimating mixed logit models with a simple minorization-maximization (MM) algorithm. The algorithm requires minimal coding and is easy to implement for a variety of mixed logit models. Most importantly, the algorithm has a very low cost per iteration relative to current methods, producing substantial computational savings. In addition, the method is asym...

In this article, we extend the discussion of prediction models of Configural Frequency Analysis (CFA). We build on the correspondence of such prediction models to logit models that was shown by von Eye and Bogat (in this issue), and derive additional possible CFA prediction models. These models can, as is illustrated, include multiple predictors and multiple criteria. Design matrices are presen...

In this paper we develop a two-step survival time approach – a discrete logit model with survival time dummies – that allows for time-varying explanatory variables and interval censored data. Our empirical analysis reveals that the twostep approach outperforms the benchmark logit model with respect to out-ofsample prediction accuracy. Survival time, however, does not play an important role. The...

This paper investigates the potential of a cellular automata (CA) model based on logistic regression (logit) and Markov Chain Monte Carlo (MCMC) to simulate the dynamics of urban growth. The model assesses urbanization likelihood based on (i) a set of urban development driving forces (calibrated based on logit) and (ii) the land-use of neighboring cells (calibrated based on MCMC). An innovative...

We present a new multi-path routing methodology, MLBrouting, that is based on the multinomial logit model, which is well known in the random utility field. The key concept of the study is to set multiple paths from the origin to the destination, and distribute packets in accordance with multinomial logit type probability. Since MLB-routing is pure multi-path routing, it reduces the convergence ...

We report the results of an experiment designed to shed light on beliefs off equilibrium paths. The experiment implements an incomplete information club game. A unique outcome results in all perfect Bayesian equilibria, but beliefs and actions off the equilibrium path are unconstrained under all concepts except extensive form rationalizability (EFR) and logit equilibrium. 90% of subject’s decis...

Recently, the Markov chain choice model has been introduced by Blanchet et al. to overcome the computational intractability for learning and revenue management for several modern choice models, including the mixed multinomial logit models. However, the known methods for learning the Markov models require almost all items to be offered in the learning stage, which is impractical. To address this...

In the past, research in traffic assignment modeling has been directed primarily towards the deterministic model. Alternative, more behavioral principles were thought to be too demanding computationally. This paper presents two mathematical contributions that enable one to solve a logit assignment model with flow-dependent travel times at a reduced cost. First, a convergence test for Fisk's min...