The multinomial logit model with random coefficients is widely used in applied research. This paper is concerned with estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters. Some of these parameters may be zero. The...

Spatial dynamic microsimulations allow for the multivariate analysis of complex socioeconomic systems with geographic segmentation. For this, a synthetic replica of the system as base population is stochastically projected into future periods. Thereby, the projection is based on micro-level...

Traffic congestion contributes to longer travel times and increased travel time variability. We account for the dynamic nature of travellers' choices, by deriving a closed-form solution for the costs of travel time variability. The resulting travel delay cost function is linear in the mean...

We consider a difference-in-differences setting with a continuous outcome, such as wages or expenditure. The standard practice is to take its logarithm and then interpret the results as an approximation of the multiplicative treat- ment effect on the original outcome. We argue that a researcher...

We propose a Simulated Maximum Likelihood estimation method for the random coefficient logit model using aggregate data, accounting for heterogeneity and endogeneity. Our method allows for two sources of randomness in observed market shares - unobserved product characteristics and sampling...

This study presents an approach to apply the maximum likelihood estimation (MLE) method to estimate the parameters in quantitative spatial economic models. The proposed method can be applied to any model in which the unique values of the error terms can be recovered from the observed data on the...

Researchers are often faced with the challenge of developing statistical models with incomplete data. Exacerbating this situation is the possibility that either the researcher’s complete-data model or the model of the missing-data mechanism is misspecified. In this article, we create a formal...

Logistic regression is a very popular binary classification technique in many industries, particularly in the financial service industry. It has been used to build credit scorecards, estimate the probability of default or churn, identify the next best product in marketing, and many more...