Bubbles and Self‐Enforcing Debt

We characterize equilibria with endogenous debt constraints for a general equilibrium economy with limited commitment in which the only consequence of default is losing the ability to borrow in future periods. First, we show that equilibrium debt limits must satisfy a simple condition that allows agents to exactly roll over existing debt period by period. Second, we provide an equivalence result, whereby the resulting set of equilibrium allocations with self‐enforcing private debt is equivalent to the allocations that are sustained with unbacked public debt or rational bubbles. In contrast to the classic result by Bulow and Rogoff (1989a), positive levels of debt are sustainable in our environment because the interest rate is sufficiently low to provide repayment incentives.     

Identification and Inference With Many Invalid Instruments

We study estimation and inference in settings where the interest is in the effect of a potentially endogenous regressor on some outcome. To address the endogeneity, we exploit the presence of additional variables. Like conventional instrumental variables, these variables are correlated with the endogenous regressor. However, unlike conventional instrumental variables, they also have direct effects on the outcome, and thus are “invalid” instruments. Our novel identifying assumption is that the direct effects of these invalid instruments are uncorrelated with the effects of the instruments on the endogenous regressor. We show that in this case the limited-information-maximum-likelihood (liml) estimator is no longer consistent, but that a modification of the bias-corrected two-stage-least-square (tsls) estimator is consistent. We also show that conventional tests for over-identifying restrictions, adapted to the many instruments setting, can be used to test for the presence of these direct effects. We recommend that empirical researchers carry out such tests and compare estimates based on liml and the modified version of bias-corrected tsls. We illustrate in the context of two applications that such practice can be illuminating, and that our novel identifying assumption has substantive empirical content.     

Constructing Optimal Instruments by First‐Stage Prediction Averaging

This paper considers model averaging as a way to construct optimal instruments for the two‐stage least squares (2SLS), limited information maximum likelihood (LIML), and Fuller estimators in the presence of many instruments. We propose averaging across least squares predictions of the endogenous variables obtained from many different choices of instruments and then use the average predicted value of the endogenous variables in the estimation stage. The weights for averaging are chosen to minimize the asymptotic mean squared error of the model averaging version of the 2SLS, LIML, or Fuller estimator. This can be done by solving a standard quadratic programming problem.     

Laplace record data

Basic properties of upper record values _XT(1),_XT(2),…,_XT(n)$X_{T(1)},X_{T(2)},\dots ,X_{T(n)}$ from a symmetric two-parameter Laplace distribution are established. In particular, unimodality of the density function and the exact expression of the mode are derived. Moreover, we obtain approximations of the first and second moment and the variance of _XT(k)$X_{T(k)}$ which provide close approximations even for moderate k. Additionally, limit laws and simulation of Laplace records are considered. Finally, we discuss maximum likelihood estimation in a location-scale family of Laplace distributions. We obtain nice representations of the estimators provided that the location parameter is unknown and present interesting properties of the established estimators. Some illustrative examples complete the presentation.     

Random Effects Estimators with many Instrumental Variables

In this paper we propose a new estimator for a model with one endogenous regressor and many instrumental variables. Our motivation comes from the recent literature on the poor properties of standard instrumental variables estimators when the instrumental variables are weakly correlated with the endogenous regressor. Our proposed estimator puts a random coefficients structure on the relation between the endogenous regressor and the instruments. The variance of the random coefficients is modelled as an unknown parameter. In addition to proposing a new estimator, our analysis yields new insights into the properties of the standard two‐stage least squares (TSLS) and limited‐information maximum likelihood (LIML) estimators in the case with many weak instruments. We show that in some interesting cases, TSLS and LIML can be approximated by maximizing the random effects likelihood subject to particular constraints. We show that statistics based on comparisons of the unconstrained estimates of these parameters to the implicit TSLS and LIML restrictions can be used to identify settings when standard large sample approximations to the distributions of TSLS and LIML are likely to perform poorly. We also show that with many weak instruments, LIML confidence intervals are likely to have under‐coverage, even though its finite sample distribution is approximately centered at the true value of the parameter. In an application with real data and simulations around this data set, the proposed estimator performs markedly better than TSLS and LIML, both in terms of coverage rate and in terms of risk.     

Confidence Intervals for Partially Identified Parameters

Recently a growing body of research has studied inference in settings where parameters of interest are partially identified. In many cases the parameter is real‐valued and the identification region is an interval whose lower and upper bounds may be estimated from sample data. For this case confidence intervals (CIs) have been proposed that cover the entire identification region with fixed probability. Here, we introduce a conceptually different type of confidence interval. Rather than cover the entire identification region with fixed probability, we propose CIs that asymptotically cover the true value of the parameter with this probability. However, the exact coverage probabilities of the simplest version of our new CIs do not converge to their nominal values uniformly across different values for the width of the identification region. To avoid the problems associated with this, we modify the proposed CI to ensure that its exact coverage probabilities do converge uniformly to their nominal values. We motivate this modified CI through exact results for the Gaussian case.     

Objective Bayes Covariate‐Adjusted Sparse Graphical Model Selection

We present an objective Bayes method for covariance selection in Gaussian multivariate regression models having a sparse regression and covariance structure, the latter being Markov with respect to a directed acyclic graph (DAG). Our procedure can be easily complemented with a variable selection step, so that variable and graphical model selection can be performed jointly. In this way, we offer a solution to a problem of growing importance especially in the area of genetical genomics (eQTL analysis). The input of our method is a single default prior, essentially involving no subjective elicitation, while its output is a closed form marginal likelihood for every covariate‐adjusted DAG model, which is constant over each class of Markov equivalent DAGs; our procedure thus naturally encompasses covariate‐adjusted decomposable graphical models. In realistic experimental studies, our method is highly competitive, especially when the number of responses is large relative to the sample size.     

Product market competition and analyst following

In this study, we investigate the link between product market competition and the extent of a firm’s analyst following. Our research is motivated by prior studies on analyst coverage, as well as the literature pertaining to the informational and monitoring role of product market competition. Using a sample of 520 French listed firms over 2000–2013, we find support to the hypothesis that the number of analysts following a firm decreases with the intensification of competition. This result suggests that the disciplinary power of product market competition makes investors more confident that firms are well monitored, which reduces their incentives to seek additional private information produced by financial analysts. In short, our findings corroborate previous evidence that there is a substitution effect between competition and other external corporate governance mechanisms, especially analyst following. Our results also shed more light on how the quality of firms’ monitoring mechanisms and information environment is affected by the intensity of product market competition.     

Ergonomics program management in Tucuruí Hydropower Plant using TPM methodology

This paper aims to present the benefits achieved in the ergonomics process management with the use of the TPM methodology (Total Productive Maintenance) in Tucuruí Hydropower Plant. The methodology is aligned with the corporate guidelines, moreover with the Strategic Planning of the company, it is represented in the TPM Pillars including the Health Pillar in which is inserted the ergonomics process. The results of the ergonomic actions demonstrated a 12% reduction over the absenteeism rate due to musculoskeletal disorders, solving 77,0% of ergonomic non-conformities, what favored the rise of the Organizational Climate in 44,8%, impacting on the overall performance of the company. Awards confirmed the success of the work by the achievement of the Award for TPM Excellence in 2001, Award for Excellence in Consistent TPM Commitment in 2009 and more recently the Special Award for TPM Achievement, 2010. The determination of the high rank administration and workers, allied with the involvement/dynamism of Pillars, has assured the success of this management practice in Tucuruí Hydropower Plant.     

Mapping of noise impact provoked by the execution of foundation piles at high rise building sites

The objective of this work is to map, in a limited area inside and outside of the worksite, the environmental impact generated by sound pollution coming from the driving of foundation piles for high rise buildings, as well as to observe and check if the noise levels produced by the emitting source are tolerable in the urban environment. The methodology of the work includes a survey of technical references about the subject; measurement of noises surrounding the worksite during the foundation phase for four distinct buildings, with different types of piles: prefabricated piles, continuous helical displacement piles , traditional compaction piles and Terra Probe compaction piles. A grid of points was built due to the time of driving and after that the measurements of environmental noises were performed emitted by the execution of each type of pile using a sound level meter. The interpretation of the measurements and their impacts on the neighborhood of the building were performed using the computational tool Suffer for creating noise level contours. The X and Y axes of the grid represent the distances in meters of the area studied and the Z axis represents the noise measured in dB. The contours developed represent the mapping of the noise at the worksites and their surroundings. The mapping of the urban impact of noise, the measurement of its dimensions, and the examination of its propagation around the building are important subsides to adequate individual and collective protection procedures. Seventy one points were measured at four building sites with different types of piles, and the results showed that at only three points was the noise within the limits of the Municipal Law of Recife of 70 dB, which proves the relevance of the research. Finally, the comparative analysis between the four types of piles shows that the continuous helical displacement pile emits the lowest noise level among the four pile types studied.