Investment and Time to Plan and Build: A Comparison of Structures vs. Equipment in A Panel of Italian Firms (original) (raw)
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Investment and Time to Plan: A Comparison of Structures vs. Equipment in a Panel of Italian Firms
SSRN Electronic Journal, 2000
Time to build" models of investment expenditures play an important role in many traditional and modern theories of the business cycle, especially for explaining the dynamic propagation of shocks. We estimate the structural parameters of a time-to-build model using firm-level investment data on equipment and structures. For equipment expenditures, we find no evidence of time-to-build effects beyond one period. For structures, by contrast, there is clear evidence of time to build in the range of 2-3 years. The contrast between equipment and structures is intuitively reasonable and consistent with previous results. The estimates for structures also indicate that initial-period expenditures are low, and increase as projects near completion. These results provide empirical support for including "time to plan" effects for investment in structures. More generally, these results suggest a potential source of specification error for Q models of investment and production-based asset pricing models that ignore the time required to plan, build and install new capital.
1995
Large physical capital stock projects need long periods to be built. But also adjustment costs of capital have demonstrated their relevance in the empirical literature. We incorporate both time-to-build and adjustment costs in factor demand models. The time-to-build specification emerges from the business cycle theory from Kydland and Prescott (1982, Econometrica) and allows for the estimation of the length of construction and the distribution of investment during this period. We identify time-to-build and adjustment costs dynamics theoretically and empirically, the first by moving averages and the latter by autoregressive terms. The empirical results show the significance of time-to-build of around three to five quarters on average in six OECD countries.
Time-to-build, monetary shocks, and aggregate fluctuations
Journal of Monetary Economics, 2006
The idea that the investment process takes time to produce finished capital goods was an integral part of Kydland and Prescott's early work on real business cycles, but this feature has been dropped in much recent work, mainly because it seemed to have little effect on macroeconomic dynamics. With a generalization of the ''time-to-build'' feature that incorporates multiple types of capital, however, a New Keynesian model can produce ''u-shaped'' responses in output, investment, and inflation to a monetary policy shock. Such responses are not found in many studies that assume no time-to-build friction. In addition, different specifications of the time-to-build structure result in substantially different response patterns for these aggregate variables.
Time-to-Build, Delivery Lags, and Equilibrium Pricing of Capital Goods
1985
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.
Why demand uncertainty curbs investment: Evidence from a panel of Italian manufacturing firms
Journal of Macroeconomics, 2010
Theoretically, the effect on investment of uncertainty over the demand for a firm's product may be unclear because of the influence of several factors, such as the production technology and the amount of competition in the product market. It has not been possible, until now, to investigate more closely the interplay of different factors in the time dimension because the empirical research has been based on cross-section analysis. This omission makes biased estimates of the investment-uncertainty relationship likely. The aim of this paper is to extend the findings of the empirical literature using a panel of Italian firms in the period 1996-2004, covering a complete business cycle. The availability of panel survey data on companies' investment plans, expected future sales and demand uncertainty allows us to account for unobservable individual differences between firms, macroeconomic shocks and the evolution of the investment-uncertainty relationship. A key finding of our paper concerns the role of the competition encountered by Italian firms in 1996-2004. The gradual loss of market power over time of Italian manufacturing firms, along with the increasing flexibility of labour input may have weakened the negative effect of uncertainty on investment decisions. We show that, in repeated cross-section estimates, the omission of firm-specific effects together with the dynamic interplay described above, would have lead to misleading conclusions about the relevance of demand uncertainty in explaining investment decisions.
Is the Time-To-Build Model Empirically Viable?
IFAC Proceedings Volumes, 1998
The time-to-build model is one possible equilibrium alternative to the standard technology shock real business cycle model. The time-to-build model is appealing because it incorporates the time required to produce, deliver and install capital into standard models in such a way that these models produce endogenous cycles. Should economists take this model seriously when seeking to explain business cycle frequency data? This paper presents a Solow model with a time-to-build lag and proves that this model produces endogenous cycles that, for reasonable parameter values, can generate empirically relevant cyclic variations in output.
Time to build capital: Revisiting investment-cash-flow sensitivities
Journal of Economic Dynamics and Control, 2011
Is cash flow important in explaining investment dynamics? A large body of empirical work argues that it is. This finding is further taken as evidence of capital market imperfections. We argue that time-to-build for capital projects creates an investment cash flow sensitivity as found in empirical studies that may not be indicative of capital market frictions. We demonstrate this using a perfect capital markets model with firms that make investment decisions in capital projects indexed by the length of the time-to-build. We show that the typical (empirical) investment regression with q and cash flow is ridden with specification error under time-to-build investment. This error is due to an omitted right hand side state variable (current expenditure on existing capital projects) that fully describes optimal investment along with marginal q and is strongly correlated with cash flow. In addition, time aggregation error can give rise to cash flow effects independently of the time-to-build effect. Importantly, both errors arise independently of potential measurement error in q.
Investment and Investment Financing in Italy: Some Evidence at the Macro Level
SSRN Electronic Journal, 2016
We analyse the developments of investment and investment financing in Italy since 1995, based on data from national accounts and the flow of funds. The exceptional fall in investment after the global financial crisis in 2007 concerned all institutional sectors and asset categories. However, appropriately deflated data highlight the more intense fall of household capital expenditure. Consistently, on the asset side, construction was one of the most hard-hit capital goods; ICT and intangible investment instead weathered the double recession better. Focusing on investment financing, the eruption of the crisis caused a major contraction in the availability of external finance for non-financial corporations and households. Long-term loans to non-financial corporations became more important, crowding out their short-term counterparts. Also the weight of debt securities increased significantly, especially after 2008.
Regional infrastructure and firm investment: theory and empirical evidence for Italy
2009
We model the channels through which public expenditure on infrastructure influences firm value and shapes its investment decisions via both adjustment costs and marginal profitability of capital. We test these hypotheses by using a large panel of Italian firms. Empirical results show that infrastructure interacts with revenues and costs in shaping firm's profitability of capital and influences its adjustment costs.
Short- and long-run heterogeneous investment dynamics
Empirical Economics, 2017
In this paper, we model the dynamics of business investment taking into account asset-specific characteristics potentially affecting the reactivity of aggregate and disaggregate capital accumulation over the business cycle. We estimate Information and Communication Technologies (ICTs) and traditional investment (non-ICT) determinants within a Vector Error Correction Model testing the assumptions of the flexible accelerator and neoclassical model as well as the role of financial constraints and uncertainty. We evaluate our model on Italian data over the period 1980-2012, and we check our results also with Spanish and UK data. Our findings support the assumption that capital is heterogeneous since short-and long-run determinants are significantly different across the assets. Traditional assets experience stock adjustment costs while ICT investment incurs flow adjustment cost. In the short run, liquidity is a key determinant of investment independently of the asset type. In the long run, uncertainty significantly affects ICT. Finally, the results of the counterfactual exercises support the idea that ICT is a key policy variable to foster economic growth.