This paper was published with no issue number.

This paper was published with no issue number.

This paper characterizes the behavior of investment expenditures, optimal capital stocks, and real interest rates in the time-to-build model of investment. These results are used to show that the delivery lag model of investment fails to account for time lags in investment when constructing the cost of capital variable and hence, misspecifies the effects of interest rates on investment expenditures. Second, this paper derives equilibrium pricing relationships involving the prices of existing capital and uses these relationships to obtain simple tests of the underlying investment technology. Despite the widespread use of 'q' in the empirical investment literature, it is shown that the relationship between current investment and an appropriately defined measure of Tobin's 'q' contains no such testable implications. Finally, it is shown that the practice of using stock market data to measure the price of existing capital is invalid when time lags exist in the investment process.

Manufacturing plants have a clear life cycle: they are born small, grow substantially as they age, and eventually die. Economists have long thought that this life cycle is driven by the accumulation of plant-specific knowledge, here called organization capital. Theory suggests that where plants are in the life cycle determines the size of the payments, or dividends, plant owners receive from organization capital. These payments are compensation for the interest cost to plant owners of waiting for their plants to grow. We build a quantitative growth model of the life cycle of plants and use it, along with U.S. data, to infer the overall size of these payments. They turn out to be quite large—more than one-third the size of the payments plant owners receive from physical capital, net of new investment, and more than 40% of payments from all forms of intangible capital.

Previous research has suggested that discrete and occasional plant-level capital adjustments have significant aggregate implications. In particular, it has been argued that changes in plants’ willingness to invest in response to aggregate shocks can at times generate large movements in total investment demand. In this study, I re-assess these predictions in a general equilibrium environment. Specifically, assuming nonconvex costs of capital adjustment, I derive generalized (S,s) adjustment rules yielding lumpy plant-level investment within an otherwise standard equilibrium business cycle model. In contrast to previous partial equilibrium analyses, model results reveal that the aggregate effects of lumpy investment are negligible. In general equilibrium, households’ preference for relatively smooth consumption profiles offsets changes in aggregate investment demand implied by the introduction of lumpy plant-level investment. As a result, adjustments in wages and interest rates yield quantity dynamics that are virtually indistinguishable from the standard model.

Recent empirical analysis has found nonlinearities to be important in understanding aggregated investment. Using an equilibrium business cycle model, we search for aggregate nonlinearities arising from the introduction of nonconvex capital adjustment costs. We find that, while such costs lead to nontrivial nonlinearities in aggregate investment demand, equilibrium investment is effectively unchanged. Our finding, based on a model in which aggregate fluctuations arise through exogenous changes in total factor productivity, is robust to the introduction of shocks to the relative price of investment goods.

We develop an equilibrium business cycle model where producers of final goods pursue generalized (S,s) inventory policies with respect to intermediate goods due to nonconvex factor adjustment costs. When calibrated to reproduce the average inventory-to-sales ratio in postwar U.S. data, our model explains over half of the cyclical variability of inventory investment. Moreover, inventory accumulation is strongly procyclical, and production is more volatile than sales, as in the data.

The comovement between inventory investment and final sales is often interpreted as evidence that inventories amplify aggregate fluctuations. In contrast, our model economy exhibits a business cycle similar to that of a comparable benchmark without inventories, though we do observe somewhat higher variability in employment, and lower variability in consumption and investment. Thus, our equilibrium analysis reveals that the presence of inventories does not substantially raise the cyclical variability of production, because it dampens movements in final sales.

This paper derives a variance bounds test for a broad class of linear rational expectations models. According to this test if observed data accords with the model, then a weighted sum of autocovariances of the covariance-stationary components of the endogenous state variables should be nonnegative. The new test reinterprets its forefather—West's [1986] variance bounds test— and extends its applicability by not requiring exogenous state variables in order to be tested. The possibility of the test's application to nonlinear models is also discussed.