Markups, Aggregation, and Inventory Adjustment
用一个多部门模型统一解释了库存文献中的两个谜题：加总数据中库存调整速度估计值很小，而企业层面数据中估计值显著更高；模型表明，忽略反周期的加成变动会导致调整速度估计向下偏误，且加总数据偏误更严重。
In this paper I suggest a unified explanation for two puzzles in the inventory literature: first, estimates of inventory speeds of adjustment in aggregate data are very small relative to the apparent rapid reaction of stocks to unanticipated variations in sales. Second, estimates of inventory speeds of adjustment in firm-level data are significantly higher than in aggregate data. The paper develops a multi-sector model where inventories are held to avoid stockouts, and price markups vary along the business cycle. The omission of countercyclical markup variations from inventory targets introduces a downward bias in estimates of adjustment speeds obtained from partial adjustment models. When the cyclicality of markups differs across sectors, this downward bias is shown to be more severe with aggregate rather than firm-level data. Similar results apply not only to inventories, but also to labor and prices. Montercarlo simulations of a calibrated version of the model suggest that these biases are quantitatively significant.