The Role of Demand Information for Price-Setting Newsvendors : A Monotone Comparative Statics Approach

In this paper, we examine the role of demand forecast information for price-setting newsvendors. In our model, the newsvendor faces stochastic demand that is price-sensitive, has the discretion of setting the retail price, and receives some informative signal that helps the quantity decision. Focusing on a specific family of signal structure which satisfies the scale-location property, we find that when the newsvendor's demand forecast gets improved, the optimal retail price can either increase, decrease, or stay the same, and we provide concrete sufficient conditions on primitives under which each sort of monotonicity prevails. The analysis utilizes the monotone comparative statics approach, which does not require the solution uniqueness and largely weakens the regularity conditions commonly adopted in the literature. We offer a comprehensive investigation of a full spectrum of information structures, whereas the extant literature focuses exclusively on the comparison between the full-information and null-information cases. We also provide several commonly adopted distribution functions as applications of this approach