Using a large sample of accounting data for non-financial companies in France, this paper studies the interactions between macroeconomic shocks and companies’ financial fragility. We consider links in both directions, namely whether firms’ bankruptcies are affected by macroeconomic variables, and whether bankruptcies determine the business cycle. We estimate forecasting equations for firms’ bankruptcy using Shumway's (2001) approach and study the joint dynamics of bankruptcies and macroeconomic variables within an exogenous VAR type model estimated at the sector level. We find evidence of reciprocal links between the bankruptcy rate and the output gap and highlight significant “second round effects” of shocks to the output gap on bankruptcies. We show how taking into account the dynamic transmission of macroeconomic shocks matters in stress testing exercises.

This paper examines granularity adjustments to parameter estimators in a default risk model with cohorts. The model is an extension of the Vasicek model (Vasicek, 1991) and includes a general factor and cohort specific factors. The granularity adjustments derived in the paper concern the mean and/or the variance of observed default frequencies and are easy to implement in practice. For illustration, the method is applied to the S&P corporate ratings. The Granularity Adjusted (GA) estimators are compared to the unadjusted estimators in terms of their asymptotic properties and in finite sample.

The delta method and continuous mapping theorem are among the most extensively used tools in asymptotic derivations in econometrics. Extensions of these methods are provided for sequences of functions that are commonly encountered in applications and where the usual methods sometimes fail. Important examples of failure arise in the use of simulation-based estimation methods such as indirect inference. The paper explores the application of these methods to the indirect inference estimator (IIE) in first order autoregressive estimation. The IIE uses a binding function that is sample size dependent. Its limit theory relies on a sequence-based delta method in the stationary case and a sequence-based implicit continuous mapping theorem in unit root and local to unity cases. The new limit theory shows that the IIE achieves much more than (partial) bias correction. It changes the limit theory of the maximum likelihood estimator (MLE) when the autoregressive coefficient is in the locality of unity, reducing the bias and the variance of the MLE without affecting the limit theory of the MLE in the stationary case. Thus, in spite of the fact that the IIE is a continuously differentiable function of the MLE, the limit distribution of the IIE is not simply a scale multiple of the MLE, but depends implicitly on the full binding function mapping. The unit root case therefore represents an important example of the failure of the delta method and shows the need for an implicit mapping extension of the continuous mapping theorem.

We develop results for the use of LASSO and Post-LASSO methods to form firststage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p, that apply even when p is much larger than the sample size, n.We rigorously develop asymptotic distribution and inference theory for the resulting IV estimators and provide conditions under which these estimators are asymptotically oracle-efficient.In simulation experiments, the LASSO-based IV estimator with a data-driven penalty performs well compared to recently advocated many-instrument-robust procedures.In an empirical example dealing with the effect of judicial eminent domain decisions on economic outcomes, the LASSObased IV estimator substantially reduces estimated standard errors allowing one to draw much more precise conclusions about the economic effects of these decisions.Optimal instruments are conditional expectations; and in developing the IV results, we also establish a series of new results for LASSO and Post-LASSO estimators of non-parametric conditional expectation functions which are of independent theoretical and practical interest.Specifically, we develop the asymptotic theory for these estimators that allows for non-Gaussian, heteroscedastic disturbances, which is important for econometric applications.By innovatively using moderate deviation theory for self-normalized sums, we provide convergence rates for these estimators that are as sharp as in the homoscedastic Gaussian case under the weak condition that log p = o(n 1/3 ).Moreover, as a practical innovation, we provide a fully data-driven method for choosing the user-specified penalty that must be provided in obtaining LASSO and Post-LASSO estimates and establish its asymptotic validity under non-Gaussian, heteroscedastic disturbances.

We propose that diversity is a malleable concept capable of being used either to attenuate or to enhance racial inequality. The research reported here suggests that when people are exposed to ambiguous information concerning an organization’s diversity, they construe diversity in a manner consistent with their social-dominance motives. Specifically, anti-egalitarian individuals broaden their construal of diversity to include nonracial (i.e., occupational) heterogeneity when an organization’s racial heterogeneity is low. By contrast, egalitarian individuals broaden their construal of diversity to include nonracial heterogeneity when an organization’s racial heterogeneity is high. The inclusion of occupational heterogeneity in perceptions of diversity allows people across the spectrum of social-dominance orientation to justify their support for or opposition to hierarchy-attenuating affirmative-action policies. Our findings suggest that diversity may not have a fixed meaning and that, without a specific delineation of what the concept means in particular contexts, people may construe diversity in a manner consistent with their social motivations.

Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

The Norton–Bass (NB) model is often credited as the pioneering multigeneration diffusion model in marketing. However, as acknowledged by the authors, when counting the number of adopters who substitute an old product generation with a new generation, the NB model does not differentiate those who have already adopted the old generation from those who have not. In this study, we develop a generalized Norton–Bass (GNB) model that separates the two different types of substitutions. The GNB model provides closed-form expressions for both the number of units in use and the adoption rate, and offers greater flexibility in parameter estimation, forecasting, and revenue projection. An appealing aspect of the GNB model is that it uses exactly the same set of parameters as the NB model and is mathematically consistent with the later. Empirical results show that the GNB model delivers better overall performance than previous models both in terms of model fit and forecasting performance. The analyses also show that differentiating leapfrogging and switching adoptions based on the GNB model can help gain additional insights into the process of multigeneration diffusion. Furthermore, we demonstrate that the GNB model can incorporate the effect of marketing mix variables on the speed of diffusion for all product generations. This paper was accepted by Pradeep Chintagunta, marketing.

In the context of a “beauty-contest” coordination game (in which pay-offs depend on the quadratic distance of actions from an unobserved state variable and from the average action), players choose how much costly attention to pay to various informative signals. Each signal has an underlying accuracy (how precisely it identifies the state) and a clarity (how easy it is to understand). The unique linear equilibrium has interesting properties: the signals which receive attention are the clearest available, even if they have poor underlying accuracy; the number of signals observed falls as the complementarity of players' actions rises; and, if actions are more complementary, the information endogenously acquired in equilibrium is more public in nature. The consequences of “rational-inattention” constraints on information transmission and processing are also studied.

Cross-border production chains tend to include geographically proximate countries. This suggests that increases in fragmentation should be largest among nearby trading partners, and thus may serve to localize gross trade. Using data on gross and value added trade from 1970-2009, we present three results supporting this conjecture. First, value added to export ratios are lower and falling more rapidly within geographic regions than between them. Second, gross trade travels shorter distances from source to destination than value added trade, and this gap is growing over time. Third, bilateral value added to export ratios have fallen most among nearby trading partners.