This paper derives a novel representation of the exponential discounting model that allows me to set identify the discount factor while accounting for ostensible deviations from optimal behavior. I interpret these deviations as a by-product of measurement error. My approach uses a revealed...

If we conducted a competition for which statistical quantity would be the most valuable in exploratory data analysis, the winner would most likely be the correlation coefficient with a significant difference from its first competitor. In addition, most data applications contain non-normal data...

We propose using the well-known conditional value at risk (CVaR) risk measure as a new methodology for incorporating robustness into portfolio optimization. Robustness in portfolio optimization can address the poor out-of-sample performance of the classical mean-variance optimization problems....

General upgrading is a strategy that a firm can upgrade a customer with any higher-end product whenever a lower-end product is out-of-stock. In this paper, we consider the capacity planning problem to decide the initial capacity for multiple products to maximize the expected total profit when...

We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution deviates from the empirical distribution. Unlike robust...

The prediction accuracy of six estimators of econometric models are compared. Two of rthe estimators are ordinary least squares (OLS) and full-information maximum likelihood. (FML). The other four estimators are robust estimators in the sense that they give less weight to large residuals. One of...

We use data on the following climate variables for the period of the last 798 thousand years: global ice volume (Icet), atmospheric carbon dioxide level (CO2,t), and Antarctic land surface temperature (Tempt). Those variables are cyclical and are driven by the following strongly exogenous...

Data-driven distributionally robust optimization is a recently emerging paradigm aimed at finding a solution that is driven by sample data but is protected against sampling errors. An increasingly popular approach, known as Wasserstein distributionally robust optimization (DRO), achieves this by...

We consider the robust single-source capacitated facility location problem with uncertainty in customer demands. A cardinality-constrained uncertainty set is assumed for the robust problem. To solve it efficiently, we propose an allocation-based formulation derived by Dantzig-Wolfe decomposition...

The minimum (worst case) value of a long-only portfolio of bonds, over a convex set of yield curves and spreads, can be estimated by its sensitivities to the points on the yield curve. We show that sensitivity based estimates are conservative, i.e., underestimate the worst case value, and that...