An Empirical Model of Inventory Investment by Durable Commodity Intermediaries

We present a new detailed data set of high-frequency observations on inventory investment by a U.S. steel wholesaler. Our analysis of the data leads to six main conclusions: orders and sales are made infrequently; orders are more volatile than sales; order sizes vary considerably; there is considerable day-to-day variability in sales prices; inventory/sales ratios are unstable; and there are occasional stockouts. We model the firm generically as a durable commodity intermediary. We demonstrate that the firm's behavior at the product level is well approximated by an optimal trading strategy derived from a multi-dimensional nonlinear dynamic programming problem with continuous state and control variables which are subject to frequently binding inequality constraints. We show that the optimal trading strategy takes the form of a generalized (S,s) rule in which the (S,s) bands are decreasing functions of the spot price. We simulate a calibrated version of this model, and show that the simulated data exhibit the key features of inventory investment we observe in our data.