This paper studies a dynamic quantile model for intertemporal decisions under uncertainty, in which the decision maker maximizes the τ-quantile of the stream of future utilities, for τ ∈ (0,1). We present two sets of contributions. First, we generalize existing results in directions that are...

This document is a follow up to the paper by Ahmed and Pesaran (2020, AP) and reports state-level forecasts for the 2024 US presidential election. It updates the 3,107 county level data used by AP and uses the same machine learning techniques as before to select the variables used in forecasting...