This paper offers a new way to estimate the relation between pay and performance. In particular, unlike previous analyses, we account for the heterogeneity that theory tells us should exist across the compensation packages of different firms. Accounting for heterogeneity allows for more efficient estimates of the pay-for-performance relation and provides a means of testing the secondary hypotheses of agency theory. Among our findings are strong evidence of inter-firm heterogeneity in compensation, even within the same industry, the existence of trade-offs in using different performance measures, and insights about the factors that influence compensation packages.

With risk-averse arbitrageurs and an effective lower bound (ELB) on nominal rates, nonlinear interactions among short-rate expectations, bond supply, and term premia emerge in equilibrium. These interactions, which are absent from affine models, help explain the observed behavior of the yield curve near the ELB, including evidence about unconventional monetary policy. The impact of both short-rate expectations and bond supply are attenuated at the ELB. However, in simulations of the post-crisis experience in the U.S., shocks to investors’ duration-risk exposures have much smaller effects than shocks to the anticipated path of short rates. The latter shocks matter, in part, because of the reduction in interest-rate volatility associated with a longer expected stay at the ELB—a novel channel of unconventional policy.

I analyze investment, q, and cash flow in a tractable stochastic model in which marginal q and average q are identically equal. I introduce classical measurement error and derive closed-form expressions for the coefficients in regressions of investment on q and cash flow. The cash-flow coefficient is positive and larger for faster growing firms, yet there are no financial frictions in the model. I develop the concepts of bivariate attenuation and weight shifting to interpret the estimated coefficients on q and cash flow in the presence of measurement error.

When estimating finance panel regressions, it is common practice to adjust standard errors for correlation either across firms or across time. These procedures are valid only if the residuals are correlated either across time or across firms, but not across both. This paper shows that it is very easy to calculate standard errors that are robust to simultaneous correlation along two dimensions, such as firms and time. The covariance estimator is equal to the estimator that clusters by firm, plus the estimator that clusters by time, minus the usual heteroskedasticity-robust ordinary least squares (OLS) covariance matrix. Any statistical package with a clustering command can be used to easily calculate these standard errors.

This study finds that managers take deviations from their target capital structures into account when planning and structuring acquisitions. Specifically, firms that are overleveraged relative to their target debt ratios are less likely to make acquisitions and are less likely to use cash in their offers. Furthermore, they acquire smaller targets and pay lower premiums. Managers of overleveraged firms also actively rebalance their capital structures when they anticipate a high likelihood of making an acquisition. Finally, they pursue the most value-enhancing acquisitions. Collectively, these findings improve understanding of how firms choose their capital structures and shed light on the interdependence of capital structure and investment decisions in the presence of financial frictions.

Understanding both the dynamics of volatility and the shape of the distribution of returns conditional on the volatility state is important for many financial applications. A simple single-factor stochastic volatility model appears to be sufficient to capture most of the dynamics. It is the shape of the conditional distribution that is the problem. This paper examines the idea of modeling this distribution as a discrete mixture of normals. The flexibility of this class of distributions provides a transparent look into the tails of the returns distribution. Model diagnostics suggest that the model, SV-mix, does a good job of capturing the salient features of the data. In a direct comparison against several affine-jump models, SV-mix is strongly preferred by Akaike and Schwarz information criteria.

An extensive collection of continuous-time models of the short-term interest rate is evaluated over data sets that have appeared previously in the literature. The analysis, which uses the simulated maximum likelihood procedure proposed by Durham and Gallant (2002), provides new insights regarding several previously unresolved questions. For single factor models, I find that the volatility, not the drift, is the critical component in model specification. Allowing for additional flexibility beyond a constant term in the drift provides negligible benefit. While constant drift would appear to imply that the short rate is nonstationary, in fact, stationarity is volatility-induced. The simple constant elasticity of volatility model fits weekly observations of the three-month Treasury bill rate remarkably well but is easily rejected when compared with more flexible volatility specifications over daily data. The methodology of Durham and Gallant can also be used to estimate stochastic volatility models. While adding the latent volatility component provides a large improvement in the likelihood for the physical process, it does little to improve bond-pricing performance.

In 1982, Dimensional Fund Advisors launched a mutual fund intended to capture the returns of small-cap stocks. The ‘9–10 Fund’ is based on the CRSP 9–10 Index, an index of small-cap stocks constituting the ninth and tenth deciles of NYSE market capitalization, although the 9–10 Fund incorporates investment rules and a trading strategy that are aimed at minimizing the potentially excessive trade costs associated with such illiquid stocks. As a result, the 9–10 Fund provided a 2.2% annual premium over the 9–10 Index for the 1982–1995 period. We show that both the investment rules and the trade strategy components of the Fund’s design contribute significantly to this return difference.