We Compare Values of Time Obtained with Linear Utility Functions Used in Logit and Hierarchical Logit Specifications of a 9-Mode Passenger Choice Model Estimated with Disaggregate Data From Santiago, Chile, and Find That They Are Sensitive to the Specification Used, Unconvincingly High, and...

Default prior choices fixing Zellner's g are predominant in the Bayesian Model Averaging literature, but tend to concentrate posterior mass on a tiny set of models. The paper demonstrates this supermodel effect and proposes to address it by a hyper-g prior, whose data-dependent shrinkage adapts...

All textbooks and articles dealing with classical tests in the context of linear models stress the implications of a significantly large F-ratio since it indicates that the mean square for whatever effect is being evaluated contains significantly more than just error variation. In general,...

Abstract Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association to a response. However, many...

This paper uses a rich Norwegian dataset to re-examine the causal relationship between family income and child outcomes. Motivated by theoretical predictions and OLS results that suggest a nonlinear relationship, we depart from previous studies in allowing the marginal effects on children's...

Time series forecasting is an important area of forecasting in which past observations of the same variable are collected and analyzed to develop a model describing the underlying relationship. The model is then used to extrapolate the time series into the future. This modeling approach is...