Conditional parameter estimates from Mixed Logit models: distributional assumptions and a free software tool

Conditional Logit Models

Relaxing the IIA Assumption in Locational Choice Models: A Comparison Between Conditional Logit, Mixed Logit, and Multinomial Probit Models∗

This paper estimates a locational choice model to assess the demand for local public services, using a data set where individuals chooses between 26 municipalities within a local labor market. We assess the importance of the IIA assumption by comparing the predictions of three difference models; the conditional logit (CL) model, the mixed logit (MXL) model, and the multinomial probit (MNP) mode...

Jump-Diffusion Processes and Affine Term Structure Models: Additional Closed-Form Approximate Solutions, Distributional Assumptions for Jumps, and Parameter Estimates

Affine term structure models in which the short rate follows a jump-diffusion process are difficult to solve, and the parameters of such models are hard to estimate. Without analytical answers to the partial difference differential equation (PDDE) for bond prices implied by jumpdiffusion processes, one must find a numerical solution to the PDDE or exactly solve an approximate PDDE. Although the...

Parameter variability and distributional assumptions in the diffusion model.

If the diffusion model (Ratcliff & McKoon, 2008) is to account for the relative speeds of correct responses and errors, it is necessary that the components of processing identified by the model vary across the trials of a task. In standard applications, the rate at which information is accumulated by the diffusion process is assumed to be normally distributed across trials, the starting point f...

Sensitivity analysis: distributional assumptions and confounding assumptions.

In a presentation of various methods for assessing the sensitivity of regression results to unmeasured confounding, Lin, Psaty, and Kronmal (1998, Biometrics54, 948-963) use a conditional independence assumption to derive algebraic relationships between the true exposure effect and the apparent exposure effect in a reduced model that does not control for the unmeasured confounding variable. How...