When empirical stock-adjustment models of manufacturers' inventories of finished goods are estimated, there appear to be two local minima in the sum of squared residuals functions. At one local minimum, the estimated adjustment speed is typically quite high; at the other, it is typically quite low. Furthermore, finding two sets of estimates that fit the data almost equally well does not appear to be a quirk of this particular application. Rather, it stems from a fundamental identification problem that afflicts partial adjustment models of all kinds. In the specific context of manufacturers' inventories of finished goods, the estimation procedure employed by Maccini and Rossana seems to pick out the solution with rapid adjustment (and high serial correlation in the disturbances) whereas the solution with slow adjustment (and little serial correlation) is more often the global minimum.