This paper proposes a robust two-stage estimation procedure for a general spatial dynamic panel data model in light of the two-stage estimation procedure in Jin et al. (2020), in which we replace the least squares estimation in its first stage by M-estimation. We also provide the justification...

This study compares the size and power of autoregressive conditional heteroskedasticity (ARCH) tests that are robust to the presence of a misspecified conditional mean. The approaches employed are based on two nonparametric regressions for the conditional mean: an ARCH test with a...

At the heart of supervised learning is a minimization problem with an objective function that evaluates a set of training data over a loss function that penalizes poor fitting and a regularization function that penalizes over-fitting to the training data. More recently, data-driven robust...

Assortment optimization arises widely in many practical applications such as retailing and online advertising. In this problem, the goal is to select a subset from a universe of substitutable products to offer customers in order to maximize the expected revenue. We study a robust assortment...

We propose robust optimization models and their tractable approximations that cater for ambiguity-averse decision makers whose underlying risk preferences are consistent with constant absolute risk aversion (CARA). Specifically, we focus on maximizing the worst-case expected exponential utility...

This paper establishes a general analytical framework for the impulse controls of the diffusion processes driven by multidimensional G-Brownian motion. We propose new G-quasi-variational inequalities (G-QVI) and we provide a verification theorem to link a classical (smooth) solution of the G-QVI...

A chance-constrained binary program (CCBP) is a general optimization problem over binary decision variables restricted by a chance constraint, which ensures that a constraint with uncertain coefficients can be violated only up to a given probability threshold. Despite its wide applications, the...