Benjamin C. Esty, William L. Megginson, Creditor Rights, Enforcement, and Debt Ownership Structure: Evidence from the Global Syndicated Loan Market, The Journal of Financial and Quantitative Analysis, Vol. 38, No. 1 (Mar., 2003), pp. 37-59

Process flexibility, whereby a production facility can produce multiple products, is a critical design consideration in multiproduct supply chains facing uncertain demand. The challenge is to determine a cost-effective flexibility configuration that is able to meet the demand with high likelihood. In this paper, we present a framework for analyzing the benefits from flexibility in multistage supply chains. We find two phenomena, stage-spanning bottlenecks and floating bottlenecks, neither of which are present in single-stage supply chains, which reduce the effectiveness of a flexibility configuration. We develop a flexibility measure g and show that increasing this measure results in greater protection from these supply-chain inefficiencies. We also identify flexibility guidelines that perform very well for multistage supply chains. These guidelines employ and adapt the single-stage chaining strategy of Jordan and Graves (1995) to multistage supply chains.

This paper characterizes empirically achievable limits for time series econometric modeling and forecasting. The approach involves the concept of minimal information loss in time series regression and the paper shows how to derive bounds that delimit the proximity of empirical measures to the true probability measure (the DGP) in models that are of econometric interest. The approach utilizes joint probability measures over the combined space of parameters and observables and the results apply for models with stationary, integrated, and cointegrated data. A theorem due to Rissanen is extended so that it applies directly to probabilities about the relative likelihood (rather than averages), a new way of proving results of the Rissanen type is demonstrated, and the Rissanen theory is extended to nonstationary time series with unit roots, near unit roots, and cointegration of unknown order. The corresponding bound for the minimal information loss in empirical work is shown not to be a constant, in general, but to be proportional to the logarithm of the determinant of the (possibility stochastic) Fisher–information matrix. In fact, the bound that determines proximity to the DGP is generally path dependent, and it depends specifically on the type as well as the number of regressors. For practical purposes, the proximity bound has the asymptotic form (K/2)log n, where K is a new dimensionality factor that depends on the nature of the data as well as the number of parameters in the model. When ‘good’ model selection principles are employed in modeling time series data, we are able to show that our proximity bound quantifies empirical limits even in situations where the models may be incorrectly specified. One of the main implications of the new result is that time trends are more costly than stochastic trends, which are more costly in turn than stationary regressors in achieving proximity to the true density. Thus, in a very real sense and quantifiable manner, the DGP is more elusive when there is nonstationarity in the data. The implications for prediction are explored and a second proximity theorem is given, which provides a bound that measures how close feasible predictors can come to the optimal predictor. Again, the bound has the asymptotic form (K/2)log n, showing that forecasting trends is fundamentally more difficult than forecasting stationary time series, even when the correct form of the model for the trends is known.

Abstract This paper explores whether analyst forecasts impound the earnings management to avoid losses and small earnings decreases documented in Burgstahler and Dichev 1997, whether analysts are able to identify which specific firms engage in such earnings management, and the implications for significant forecast error anomalies at zero earnings and zero forecast earnings. We use data from Zacks Investment Research 1999 and find that analysts anticipate earnings management to avoid small losses and small earnings decreases. Further, analysts are much more likely to forecast zero earnings than firms are to realize zero earnings, and analysts are unable to consistently identify the specific firms that engage in earnings management to avoid small losses. This latter inability contributes to significant forecast pessimism associated with zero reported earnings and significant forecast optimism associated with zero earnings forecasts.

Abstract We find that institutional ownership concentration is positively related to the pay‐for‐performance sensitivity of executive compensation and negatively related to the level of compensation, even after controlling for firm size, industry, investment opportunities, and performance. These results suggest that the institutions serve a monitoring role in mitigating the agency problem between shareholders and managers. Additionally, we find that clientele effects exist among institutions for firms with certain compensation structures, suggesting that institutions also influence compensation structures through their preferences.

We developed and tested a model in which supervisors' procedural justice perceptions lead to supervisors' organizational citizenship behavior (OCB), supervisors' OCB leads to subordinates' procedural justice perceptions, and in turn, subordinates' procedural justice perceptions directly influence subordinates' OCB. Using data collected from 373 National Guard members and their military supervisors, we found support for the mediation framework as well as for the prediction that individuals' OCB role definitions (that is, the extent to which they define OCB as extrarole) moderate the relationships between justice perceptions and OCB performance.

We study a known negative signal, the sale of insider shares in an IPO and find that insiders adopt two concealment strategies consistent with wealth-maximizing behavior. First, insiders underreport the number of personally owned shares in the prominent original prospectus and use an obscure amendment to communicate the true higher level of shares to be offered. Second, when insiders increase shares in a later amendment, they tend to either increase secondary shares disproportional to primary share increases, or to reduce primary shares to wholly or partly conceal the increase in secondary shares offered. Insiders confound the negative secondary share signal by simultaneously sending a positive lockup signal.

This paper makes two contributions to the empirical literature on agglomeration economies. First, the paper uses a unique and rich database in conjunction with mapping software to measure the geographic extent of agglomerative externalities. Previous papers have been forced to assume that agglomeration economies are club goods that operate at a metropolitan scale. Second, the paper tests for the existence of organizational agglomeration economies of the kind studied qualitatively by Saxenian (1994). This is a potentially important source of increasing returns that previous empirical work has not considered. Results indicate that localization economies attenuate rapidly and that industrial organization affects the benefits of agglomeration.

A class of model-based filters for extracting trends and cycles in economic time series is presented. These lowpass and bandpass filters are derived in a mutually consistent manner as the joint solution to a signal extraction problem in an unobserved-components model. The resulting trends and cycles are computed in finite samples using the Kalman filter and associated smoother. The filters form a class which is a generalization of the class of Butterworth filters, widely used in engineering. They are very flexible and have the important property of allowing relatively smooth cycles to be extracted from economic time series. Perfectly sharp, or ideal, bandpass filters emerge as a limiting case. Applying the method to quarterly series on U.S. investment and GDP shows a clearly defined cycle.

Many refinements of Nash equilibrium yield solution correspondences that do not have closed graph in the space of payoffs or information. This has significance for implementation theory, especially under complete information. If a planner is concerned that all equilibria of his mechanism yield a desired outcome, and entertains the possibility that players may have even the slightest uncertainty about payoffs, then the planner should insist on a solution concept with closed graph. We show that this requirement entails substantial restrictions on the set of implementable social choice rules. In particular, when preferences are strict (or more generally, hedonic), while almost any social choice function can be implemented in undominated Nash equilibrium, only monotonic social choice functions can be implemented in the closure of the undominated Nash correspondence.