Inventory management is one central problem of operations. The classical inventory theories typically focus on the operational tradeoffs to optimize the inventory decisions. In practice, however, many firms are public that often have short-term interests in their market value. In this research, we aim to understand two possible effects of stock market reaction on firms - inventory operations in the presence of asymmetric information. First, a firm's ordering decision can signal information of its demand prospect to which investors might react strongly in specific events.

In this article, we introduce the concept of model confidence bounds (MCBs) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCBs identify two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence.

Many models in economics and operations research can be formulated as Variational Inequalities (VI) problems. In this paper, we introduce an equivalence relation called ordinal equivalence transformation (OET) on VI, which has the property of preserving the solution set of VI. We revisit many classical network games in the economics literature, which include games with uni-dimensional or multi-dimensional strategies, games with strategic complementary or substitutes, games with linear or nonlinear best-reply functions, etc.

This paper develops an unbiased Monte Carlo estimator of the transition density of a multivariate jump-diffusion process. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and non-affine. It is not necessary that the volatility matrix can be diagonalized using a change of variable or change of time. Our density estimator facilitates the parametric estimation of multivariate jump-diffusion models based on discretely observed data.

Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a nonlinear fashion, such as when the response is binary. Although this modeling problem has been extensively studied, it remains unclear how to effectively control the fraction of false discoveries even in high-dimensional logistic regression, not to mention general high-dimensional nonlinear models.

To cook a delicious dish, three (3) components are required—good cooking materials, good kitchen cookware, and good cooking skills. This is the same as doing a Big Data research. To do a good job in Big Data analysis, a reliable data is your material, a powerful computer is your cookware, and the most important cooking skill is in fact the statistical methodology. It is worth mentioning that the cooking material and cookware are buyable, if you are willing to pay. However, the cooking skill (Statistical thinking and methodology) needs to be learnt by yourself.

Identification through heteroskedasticity in heteroskedastic simultaneous equations models (HSEMs) is considered. The possibility that heteroskedasticity identifies the structural parameters only partially is explicitly allowed for. The asymptotic properties of the identified parameters are derived. Moreover, tests for identification through heteroscedasticity are developed and their asymptotic distributions are derived. Monte Carlo simulations are used to explore the small sample properties of the asymptotically valid methods.

Start-up companies play an important role in innovation and many of them have become the leaders of their fields. With limited capital and human resources, start-up companies face a range of challenges in managing their research and development (R&D) projects. This presentation covers three topics in relation to R&D project management. First, we consider how to design appropriate project selection standard in a principal -agent model where the agent prefers to maximize the subsidiary profit but the headquarters prefers to maximize the consolidate profit.

Social media platforms for healthcare services are changing how patients choose doctors. The digitization of healthcare reviews has enabled patients to thoroughly evaluate doctors before booking an appointment, and has increased the transparency of the relationship between patients and doctors. In this paper, we wish to derive the impact of online information on patient choice of outpatient care doctors. We are especially interested in how operational factors influence demand.

We create a quality value chain network concept to analyze the impact of supply chain quality (SCQ) on the customer lifetime value (CLV). We apply our framework to a rich dataset from a major restaurant chain utilizing text analysis of the complaints to measure SCQ, a two-stage least squares (2SLS) model with instruments to assess the impact of SCQ on customer experience, and a structural model of consumer purchasing behavior to eventually link customer experience to CLV.