Corporate financial distress: The case of publicly listed firms in an emerging market economy

Occurrences of financial distress (FD) are not readily obvious yet can span several periods. This paper examines episodes of FD using industry‐relative (IR) firm‐/ accounting‐, market‐ and macro‐level information. Mixed logit regressions reveal that firm‐ and market‐based measures, as well as macro‐level variables explain the likelihood of FD in 263 publicly listed non‐banking firms in the Philippines during the period 1995 to 2018. Rates of identification of firms in financial distressed states of close to 69 percent are obtained at a cutoff probability of 0.30 in the model with time‐varying intercept and slope. This study shows the importance of recognizing heterogeneous firm behavior. The ability to more accurately predict the probability of FD and to determine the financial health of firms can help financial institutions in allocating funds and policy makers in predicting crises episodes.     

Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models

There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed models where interest is on the variance components, we study the effects of random effects misspecification on random effects inference in linear mixed models, for which there is considerably less literature. Our findings are surprising and contrary to general belief: for point estimation, maximum likelihood estimation of the variance components under misspecification is consistent, although in finite samples, both the bias and mean squared error can be substantial. For inference, we show through theory and simulation that under misspecification, standard likelihood ratio tests of truly non‐zero variance components can suffer from severely inflated type I errors, and confidence intervals for the variance components can exhibit considerable under coverage. Furthermore, neither of these problems vanish asymptotically with increasing the number of clusters or cluster size. These results have major implications for random effects inference, especially if the true random effects distribution is heavier tailed than the normal. Fortunately, simple graphical and goodness‐of‐fit measures of the random effects predictions appear to have reasonable power at detecting misspecification. We apply linear mixed models to a survey of more than 4 000 high school students within 100 schools and analyse how mathematics achievement scores vary with student attributes and across different schools. The application demonstrates the sensitivity of mixed model inference to the true but unknown random effects distribution.     

Some general results for moments in bivariate distributions

The purpose of this paper is to present a closed formula to compute the moments of a general function from the knowledge of its bivariate survival function. The result is derived by utilizing an integration by parts formula for two variables, which is not readily available in the literature. Many of the existing results are obtained as special cases. Finally, two examples are presented to illustrate the results. In both the examples, mixed moments as well as moments for the series system and parallel system are obtained. The integration by parts formula in two variables, derived here, is of interest in its own right and we hope that it will be useful in other investigations. The integration by parts formula in two variables is derived as a special case of a general formula in n variables.     

Economic freedom and public,non-market institutions: evidence from criminal prosecution

Economic freedom, which measures the protection of property and freedom to contract, is generally argued to capture the quality of a state’s institutions regarding market activity. As to be expected, numerous studies have found that economic freedom is associated with good economic outcomes. Additionally, much effort in public economics has worked to identify the features of quality non-market public institutions. No effort has been made to connect institutions that influence market activity and institutions that govern non-market activities. We take a first step. We employ a linear programming method for measuring relative efficiencies known as Data Envelopment Analysis. We apply this technique to information on the use of inputs to the production of the prosecution of crime across the thousands of local prosecutor offices in the U.S. We then compare state-level measurements of prosecution productivity with data on state-level economic freedom from the Economic Freedom of North America index. We show that there is a positive and statistically significant relationship between the two. Those states that develop institutions respecting economic freedom also tend to be the states that develop efficient publicly-provided services. The results are extended to complementary economic freedom measurements.     

An iterative computational scheme for solving the coupled Hamilton–Jacobi–Isaacs equations in nonzero-sum differential games of affine nonlinear systems

In this paper, we present iterative or successive approximation methods for solving the coupled Hamilton–Jacobi–Isaacs equations (HJIEs) arising in nonzero-sum differential game for affine nonlinear systems. We particularly consider the ones arising in mixed \({\mathcal H}_{2}/{\mathcal H}_{\infty }\) control. However, the approach is perfectly general and can be applied to any others including those arising in the N-player case. The convergence of the method is established under fairly mild assumptions, and examples are solved to demonstrate the utility of the method. The results are also specialized to the coupled algebraic Riccati equations arising typically in mixed \({\mathcal H}_{2}/{\mathcal H}_{\infty }\) linear control. In this case, a bound within which the optimal solution lies is established. Finally, based on the iterative approach developed, a local existence result for the solution of the coupled-HJIEs is also established.     

The long-run performance of diversifying firms

The corporate diversification literature presents a puzzle. Short-horizon event studies report positive abnormal returns around the announcement of a diversifying event, while studies that examine diversified firms find evidence that diversified firms are worth less than specialized firms (a diversification discount). If diversification is value destroying, perhaps the destruction occurs over longer periods than have been previously tested. This paper tests the hypothesis that diversifying firms have negative long-run abnormal performance following diversification by examining a sample of specialized firms that have a diversifying event from 1978 through 1998. The firms are tracked for up to five years past their diversification year. There is evidence that value is destroyed for small firms that diversify but enhanced for larger firms that diversify.

A response to the critiques of DEA by Dmitruk and Koshevoy, and Bol

We here critique the articles by Dmitruk & Koshevoy (1991, J Econ Theory 55:121–144) and by Bol (1986, J Econ Theory 38:380–385) by showing how to solve the examples they erected to show the non-existence of functions for evaluating performance efficiencies in DEA. We also show that functions satisfying these criteria—and other important criteria as well—were already available prior to the publications of D&K and by Bol and have since been greatly extended to increase the power and scope of DEA.