Journal Article Delivery Lags and the Demand for Investment Get access Louis J. Maccini Louis J. Maccini The Johns Hopkins University Search for other works by this author on: Oxford Academic Google Scholar The Review of Economic Studies, Volume 40, Issue 2, April 1973, Pages 269–281, https://doi.org/10.2307/2296653 Published: 01 April 1973

I. Introduction, 177. — II. The representative firm model, 179. —III. Aggregate model, 185. — IV. Implications of the model, 192.

source and the extent of lags in the response of prices to changes in various explanatory variables. Knowledge of the relevant lag patterns is of obvious importance for forecasting movements in prices and for formulating policies that are designed to reduce the rate of inflation. In the recent empirical literature on prices, the model most commonly used to specify price equations is the static neo-classical model of price formation.1 This model presumes that the representative firm behaves as if it were a simple monopolist that faces competitive factor input markets. The firm is assumed to set its price so as to maximize current profits. The model yields the proposition that the firm's long-run optimal price depends on expected normal factor input prices and variables, e.g., expected real income, that shift the firm's demand curve. Lags are introduced into the pricing process of this model in two ways. Some authors2 focus on expectation formation lags whereby normal factor input prices or variables that shift the firm's demand curve are related through distributed lags to past values of the variable being forecast. Often, the distributed lag is assumed to be

The effects of demand and price expectations on price behavior are analyzed, using data from the manufacturing sector and a model formulated to pinpoint the demand-oriented factors affecting prices and to incorporate price expectations as a direct influence on price behavior. The expected normal demand is seen to have a strong and systemic influence on price behavior. The expected normal level of new orders is found to be an adequate measure with sector data in general, although some industries require more specification. The effects of inventories and unfilled orders on prices are sporadic and across sectors and industries. Demand-oriented forces are concluded to perform as well statistically as cost-push determinants. Expectations concerning the current normal industry price level have a significant and consistent impact on price behavior. This supports the idea that firms setting prices take into account their estimates of competitor's prices. Future price expectations appear to have little effect on current price information. 15 references.

It is well known that fluctuations in inventory investment are a major source of fluctuations in gross national product. This has stimulated numerous empirical studies designed to understand the causes of changes in inventory investment. These studies include those (for example, Michael Lovell) that utilize a flexible accelerator model of inventory behavior as well as those (for example, David Belsley) that utilize the linear decision rule approach to optimal inventory holding. The latter approach, however, can be interpreted in terms of a flexible accelerator model. See J. C. R. Rowley and P. K. Trivedi for a survey of the literature. Recently, several authors (for example, Martin Feldstein and Alan Auerbach) observed that these studies yield very implausible empirical results. The basic difficulty is that the estimates of the speed with which firms close gaps between desired and actual inventory stocks are very low. Often, the estimated speed of adjustment is less than 10 percent per quarter. As Feldstein and Auerbach stress, this is extremely implausible when even the largest swings in inventories in a given quarter amount to less than one day's production. The purpose of this paper is to undertake an analysis of adjustment speeds for finished goods inventory investment. In our empirical work, we utilize a modified flexible accelerator model of inventory investment.1 Like the conventional flexible accelerator, the model permits firms to close a fraction of the gap between desired and actual inventories in any period. The relevant fraction is of course the adjustment coefficient, and it may vary in principle between zero and unity. Our model differs from the conventional model in assuming that the desired stock of finished goods inventories depends on the normal levels of exogenous variables that firms must forecast to make decisions on inventory holdings. These include not only the normal level or orders, or demand, which is a standard explanatory variable in the empirical literature, but also the normal levels of real factor-input prices and real interest rates. In addition, we permit the normal levels to change relatively slowly in response to changes in past levels of orders, real factor-input prices, and real interest rates. Our objective is to investigate whether the slow adjustment speeds that have been estimated in the literature are due to the use of models which contain an incomplete menu of exogenous variables to determine desired inventories and pay inadequate attention to lags in the adjustment of normal levels of exogenous variables.

This paper presents a model that provides an explanation, based on regime switching in the real interest rate and learning, of why tests based on stock-adjustment models, Euler equations, or decision rules—which emphasize short-run fluctuations in inventories and the interest rate—are unlikely to uncover a negative relationship between inventories and the real interest rate. The model, however, predicts that inventories will respond to long-run movements, that is, to regime shifts in the real interest rate. Tests emphasizing cointegration techniques confirm this prediction and show a significant long-run relationship between inventories and the real interest rate.