Toni M. Whited

Measurement Error and the Relationship between Investment and Q

Many recent empirical investment studies have found that the investment of financially constrained firms responds strongly to cash flow. Paralleling these findings is the disappointing performance of the q theory of investment: even though marginal q should summarize the effects of all factors relevant to the investment decision, cash flow still matters. We examine whether this failure is due to error in measuring marginal q. Using measurement errorconsistent generalized method of moments estimators, we find that most of the stylized facts produced by investment-q cash flow regressions are artifacts of measurement error. Cash flow does not matter, even for financially constrained firms, and despite its simple structure, q theory has good explanatory power once purged of measurement error.

Endogeneity in Empirical Corporate Finance1

This chapter discusses how applied researchers in corporate finance can address endogeneity concerns. We begin by reviewing the sources of endogeneity—omitted variables, simultaneity, and measurement error—and their implications for inference. We then discuss in detail a number of econometric techniques aimed at addressing endogeneity problems, including instrumental variables, difference-in-differences estimators, regression discontinuity design, matching methods, panel data methods, and higher order moments estimators. The unifying themes of our discussion are the emphasis on intuition and the applications to corporate finance.

Investment-Based Expected Stock Returns

We derive and test q‐theory implications for cross‐sectional stock returns. Under constant returns to scale, stock returns equal levered investment returns, which are tied directly to firm characteristics. When we use generalized method of moments to match average levered investment returns to average observed stock returns, the model captures the average stock returns of portfolios sorted by earnings surprises, book‐to‐market equity, and capital investment. When we try to match expected returns and return variances simultaneously, the variances predicted in the model are largely comparable to those observed in the data. However, the resulting expected return errors are large.

Capital Structure Dynamics and Transitory Debt

We estimate a dynamic capital structure model in which firms have permanent leverage targets, yet respond to shocks to investment opportunities by incurring transitory debt obligations that represent deliberate, but temporary, deviations from target. The model yields a variety of testable predictions about the link between capital structure and attributes of firms’ investment opportunities, including the volatility and serial correlation of investment opportunity shocks, the marginal profitability of investment, and the nature of capital stock adjustment costs. Target capital structures reflect the value of the option to issue transitory debt, and the average outstanding amount of debt differs predictably from target because the optimal leverage path reflects time-varying (and state-contingent) transitory debt, whose usage is determined by the firm-specific properties of current and prospective shocks to investment opportunities. Sluggish mean reversion in leverage reflects the opportunity cost of utilizing debt capacity, and not transactions costs of debt issuance, which are zero in our model.

TWO-STEP GMM ESTIMATION OF THE ERRORS-IN-VARIABLES MODEL USING HIGH-ORDER MOMENTS

We consider a multiple mismeasured regressor errors-in-variables model where the measurement and equation errors are independent and have moments of every order but otherwise are arbitrarily distributed. We present parsimonious two-step generalized method of moments (GMM) estimators that exploit overidentifying information contained in the high-order moments of residuals obtained by “partialling out†perfectly measured regressors. Using high-order moments requires that the GMM covariance matrices be adjusted to account for the use of estimated residuals instead of true residuals defined by population projections. This adjustment is also needed to determine the optimal GMM estimator. The estimators perform well in Monte Carlo simulations and in some cases minimize mean absolute error by using moments up to seventh order. We also determine the distributions for functions that depend on both a GMM estimate and a statistic not jointly estimated with the GMM estimate.

Which Firms Follow the Market? An Analysis of Corporate Investment Decisions

We test whether stock market mispricing or private investor information in stock prices affects corporate investment. We develop an econometric methodology that disentangles stock-price movements that are relevant for investment from those that are not. We combine this decomposition with proxies for private information and mispricing to devise unbiased tests for the effects of mispricing and information on investment. We depart from much of the literature by finding that stock market mispricing does not affect investment, especially that of large firms and firms subject to mispricing. In contrast, we confirm previous evidence that managers incorporate private investor information when making investment decisions. The Author 2010. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.

Dynamic Models and Structural Estimation in Corporate Finance

We review the last two decades of research in dynamic corporate finance, focusing on capital structure and the financing of investment. We first cover continuous time contingent claims models, starting with real options models, and working through static and dynamic capital structure models. We then move on to corporate financing models based on discrete-time dynamic investment problems. We cover the basic model with no financing, as well as more elaborate models that include features such as costly external finance, cash holding, and both safe and risky debt. For all the models, we offer a minimalist, simplified presentation with a great deal of intuition. Throughout, we show how these models can answer questions concerning the effects of financial constraints on investment, the level of corporate leverage, the speed of adjustment of leverage to its target, and market timing, among others. Finally, we review and explain structural estimation of corporate finance models.

Why Do Investment Euler Equations Fail

This article isolates sources of misspecification in neoclassical investment Euler equations without ad hoc alterations of the basic model. First, allowing for nonlinear marginal investment adjustment costs improves model performance slightly. Some further improvement results from isolating firms whose optimality conditions hold even in the presence of fixed costs of adjustment or costly reversibility. Finally, I identify which instruments contribute to model failure via standard GMM-based tests and also via the empirical likelihood estimator of Imbens, which allows testing overidentifying restrictions individually. Both methods show that financial instruments contribute to rejection of the overidentifying restrictions for all firms; however, only the empirical likelihood estimator shows that they are a source of failure for firms that attain an interior optimum.

Threshold Events and Identification: A Study of Cash Shortfalls

Threshold events are discrete events triggered when an observable continuous variable passes a known threshold. We demonstrate how to use threshold events as identification strategies by revisiting the evidence in Rauh (2006) that mandatory pension contributions cause investment declines. Rauhs result stems from heavily underfunded firms that constitute a small fraction of the sample and that differ from the rest of the sample in important ways; that is, the control group differs from the treated group. To alleviate this issue, we use observations near funding thresholds and find causal effects of mandatory contributions on receivables, R&D, and hiring, but not on investment. We also provide useful suggestions and diagnostics for analyzing threshold events.

Agency Conflicts and Cash: Estimates from a Dynamic Model

We estimate a dynamic model of firm investment and cash accumulation to ascertain whether agency problems affect corporate cash holding decisions. We model four specific mechanisms that misalign managerial and shareholder incentives: managerial bonuses based on current profits, limited managerial ownership of the firm, a managerial preference for firm size, and managerial perquisite consumption. Interestingly, we find nonmonotonic relations between features of cash policy and both perquisites and size preference. Our estimates indicate that agency issues related to perquisites are more important for explaining corporate cash holding than issues related to empire building. We also find that firms with lower blockholder and institutional ownership have higher managerial perquisite consumption. These agency problems result in a 22% increase in cash and a loss to equity holders of approximately 6%.