Since the underlying economic structure is likely to be affected by changes in preferences, technologies, policies, crises, etc., data in the previous time period may be irrelevant to the present data-generation process. Thus, econometric forecasts are often based on rolling estimation. However, it

Choosing the number of mixture components remains a central but elusive challenge. Traditional model selection criteria can be either overly liberal or conservative when enforcing parsimony. They may also result in poorly separated components of limited practical use. Non-local priors (NLPs) are a

In this work we propose one of the first models of "product framing" and pricing. Product framing refers to the way in which consumer choice is influenced by how products are framed, or displayed. We introduce a model in which a set of products are organized into a set of virtual pages.

In this paper, we model an online platform for short-term rentals of housing assets and analyze how a change in the pricing strategy due to the growing influx of corporate players to the platform affects the performance and payoffs of stakeholders. Many short-term online house rental platforms like

We examine whether firm internal governance with respect to headquarter-plant monitoring affects workplace safety. We use travel time reductions due to new airline routes as exogenous shocks to the cost of monitoring, and find that workplace safety improves as a result of greater proximity between

Modern decision analytics frequently involves the optimization of an objective over a finite horizon where the functional form of the objective is unknown. The decision analyst observes covariates and tries to learn and optimize the objective by experimenting with the decision variables. We present

We synthesize the field of machine learning with the canonical problem of empirical asset pricing: measuring asset risk premia. In the familiar empirical setting of cross section and time series stock return prediction, we perform a comparative analysis of methods in the machine learning

We study a service system which does not have the capability of monitoring and disclosing its real-time congestion level. However, the customers can observe and post their observations online, and future arrivals can take into account such user-generated information when deciding whether to go to

In this paper, we offer a new network perspective on one of the central topics in Operations Management - demand variability and the bullwhip effect (BWE). The topic has both practical and scholarly implications. We start with a puzzling observation: while the traditional intra-firm measure of the

Expanding on the zero-inflated Poisson (ZIP) model, the multiple-inflated Poisson (MIP) model is applied to analyze count data with multiple inflated values. The existing studies on the MIP model determined the inflated values by inspecting the histogram of count response and fitting the model with