Noisy Share Prices and the Q Model of Investment

We consider to what extent the empirical failings of the Q model of investment can be attributed to the assumption that stock market valuations accurately measure the present value of future net distributions to shareholders. We characterise the implications of different types of measurement error in the conventional average Q ratio that can result from the failure of this assumption, and show that plausible forms of measurement error can result in failure to identify the model's structural parameters. To explore this empirically, we use securities analysts' consensus earnings forecasts to construct an alternative measure of average Q, not based on share prices. Using this measure, we find more reasonable estimates of the size of adjustment costs, no significant cash flow effects, and no evidence of non-linearities. Indeed we find that our measure of average Q is a sufficient statistic for investment, and there is no additional information relevant for investment in stock market valuations. These results suggest that there are highly persistent deviations between stock market values and firms' fundamental valuations, and that these deviations are themselves correlated with fundamentals. This is consistent with rational bubble or noise trader models of share prices.

Adjustment Costs and the Identification of Cobb Douglas Production Functions

Cobb Douglas production function parameters are not identified from cross-section variation when inputs are perfectly flexible and chosen optimally, and input prices are common to all firms. We consider the role of adjustment costs for inputs in identifying these parameters in this context. The presence of adjustment costs for all inputs allows production function parameters to be identified, even in the absence of variation in input prices. This source of identification appears to be quite fragile when adjustment costs are deterministic, but more useful in the case of stochastic adjustment costs. We illustrate these issues using simulated production data.

An odyssey into local refinement and multilevel preconditioning III: Implementation and numerical experiments

In this paper, we examine a number of additive and multiplicative multilevel iterative methods and preconditioners in the setting of two-dimensional local mesh refinement. While standard multilevel methods are effective for uniform refinement-based discretizations of elliptic equations, they tend to be less effective for algebraic systems, which arise from discretizations on locally refined meshes, losing their optimal behavior in both storage and computational complexity. Our primary focus here is on Bramble, Pasciak, and Xu (BPX)-style additive and multiplicative multilevel preconditioners, and on various stabilizations of the additive and multiplicative hierarchical basis (HB) method, and their use in the local mesh refinement setting. In parts I and II of this trilogy, it was shown that both BPX and wavelet stabilizations of HB have uniformly bounded condition numbers on several classes of locally refined two- and three-dimensional meshes based on fairly standard (and easily implementable) red and red-green mesh refinement algorithms. In this third part of the trilogy, we describe in detail the implementation of these types of algorithms, including detailed discussions of the data structures and traversal algorithms we employ for obtaining optimal storage and computational complexity in our implementations. We show how each of the algorithms can be implemented using standard data types, available in languages such as C and FORTRAN, so that the resulting algorithms have optimal (linear) storage requirements, and so that the resulting multilevel method or preconditioner can be applied with optimal (linear) computational costs. We have successfully used these data structure ideas for both MATLAB and C implementations using the FEtk, an open source finite element software package. We finish the paper with a sequence of numerical experiments illustrating the effectiveness of a number of BPX and stabilized HB variants for several examples requiring local refinement

Natural resources, export structure and investment

We present cross-country empirical evidence on the role of natural resources in explaining long-run differences in private investment as a share of GDP in a sample of 72 developing countries. Our empirical results suggest important differences between oil and non-oil resources. While revenue from oil exports tends to increase private (and public) investment, there is also a robust negative effect from a measure of export concentration. After controlling for these two aspects of export structure, there is little additional information in other measures of resource abundance, or in other suggested investment determinants, such as measures of the quality of institutions, political instability or macroeconomic volatility.

GMM Estimation of Empirical Growth Models

This paper highlights a problem in using the first-difference GMM panel data estimator cross-country growth regressions. When the time series are persistent, the first-differenced GMM estimator can be poorly behaved, since lagged levels of the series provide only weak instruments for subsequent first-differences. Revisiting the work of Caselli, Esquivel and Lefort (1996), we show that this problem may be serious in practice. We suggest using a more efficient GMM estimator that exploits stationarity restrictions, and this approach is shown to give more reasonable results than first-differenced GMM in our estimation of an empirical growth model.convergence, growth, generalised method of moments, weak instruments.

Asymmetry, Loss Aversion and Forecasting

Conditional volatility models, such as GARCH, have been used extensively in financial applications to capture predictable variation in the second moment of asset returns. However, with recent theoretical literature emphasising the loss averse nature of agents, this paper considers models which capture time variation in the second lower partial moment. Utility based evaluation is carried out on several approaches to modelling the conditional second order lower partial moment (or semi-variance), including distribution and regime based models. The findings show that when agents are loss averse, there are utility gains to be made from using models which explicitly capture this feature (rather than trying to approximate using symmetric volatility models). In general direct approaches to modelling the semi-variance are preferred to distribution based models. These results are relevant to risk management and help to link the theoretical discussion on loss aversion to emprical modellingAsymmetry, loss aversion, semi-variance, volatility models.

Investment, R&D and Financial Constraints in Britain and Germany

This paper tests for the importance of cash flow on investment in fixed capital and R&D using firm-level panel data in two countries between 1985 and 1994. For German firms, cash flow is not informative in simple econometric models of fixed investment or R&D. In identical specifications for British firms, cash flow is informative about investment, although not about the level of R&D spending conditional on the R&D participation decision. In the UK, we also find that investment is less sensitive to cash flow for R&Dperforming firms, and that cash flow predicts whether firms perform R&D or not. We confirm that these differences do not simply reflect a greater role for current cash flow in forecasting future sales. These results suggest that financial constraints are more significant in Britain, that they affect the decision to engage in R&D rather than the level of R&D spending by participants, and that consequently the British firms that do engage in R&D are a self-selected group where financing constraints tend to be less binding.Investment, R&D, cash flow, financial constraints, panel data

Uncertainty and Investment Dynamics

This paper shows that, with (partial) irreversibility, higher uncertainty reduces the impact effect of demand shocks on investment. Uncertainty increases real option values making firms more cautious when investing or disinvesting. This is confirmed both numerically for a model with a rich mix of adjustment costs, time-varying uncertainty, and aggregation over investment decisions and time, and also empirically for a panel of manufacturing firms. These cautionary effects of uncertainty are large %u2013 going from the lower quartile to the upper quartile of the uncertainty distribution typically halves the first year investment response to demand shocks. This implies the responsiveness of firms to any given policy stimulus may be much lower in periods of high uncertainty, such as after major shocks like OPEC I and 9/11.