Instrumental variable estimation of heteroskedasticity adaptive error component models

The linear panel data estimator proposed by Hausman and Taylor relaxes the hypothesis of exogenous regressors that is assumed by generalized least squares methods but, unlike the Fixed Effects estimator, it can handle endogenous time invariant explanatory variables in the regression equation. One of the assumptions underlying the estimator is the homoskedasticity of the error components. This can be restrictive in applications, and therefore in this paper the assumption is relaxed and more efficient adaptive versions of the estimator are presented.     

A Note on Nonlinear Cointegration,Misspecification, and Bimodality

We derive the asymptotic distribution of the ordinary least squares estimator in a regression with cointegrated variables under misspecification and/or nonlinearity in the regressors. We show that, under some circumstances, the order of convergence of the estimator changes and the asymptotic distribution is non-standard. The t-statistic might also diverge. A simple case arises when the intercept is erroneously omitted from the estimated model or in nonlinear-in-variables models with endogenous regressors. In the latter case, a solution is to use an instrumental variable estimator. The core results in this paper also generalise to more complicated nonlinear models involving integrated time series.     

Estimation and inference in regression discontinuity designs with asymmetric kernels

We study the behaviour of the Wald estimator of causal effects in regression discontinuity design when local linear regression (LLR) methods are combined with an asymmetric gamma kernel. We show that the resulting statistic is no more complex to implement than existing methods, remains consistent at the usual non-parametric rate, and maintains an asymptotic normal distribution but, crucially, has bias and variance that do not depend on kernel-related constants. As a result, the new estimator is more efficient and yields more reliable inference. A limited Monte Carlo experiment is used to illustrate the efficiency gains. As a by product of the main discussion, we extend previous published work by establishing the asymptotic normality of the LLR estimator with a gamma kernel. Finally, the new method is used in a substantive application.     

Trust in leadership: A multi-level review and integration

Leaders have been argued to play a key role in determining organizational effectiveness across all levels (e.g., individual, team, unit) that exist within organizations. A key component in a leader's ability to be effective within such environments is the degree to which subordinates and co-workers trust him/her. Therefore, it is not surprising that researchers and practitioners alike are interested in identifying the mechanisms through which trust in leadership can be developed as well as those factors which moderate this relationship [e.g., Gillespie, N. A., Mann, L. (2004). Transformational leadership and shared values: The building blocks of trust. Journal of Managerial Psychology, 19, 588–607; Kouzes and Posner, 1995; Roberts, K. H., O'Reilly, C. A. (1974). Failures in upward communication in organizations: Three possible culprits. Academy of Management Journal, 17, 205–215; Whitener, E. M. (1997). The impact of human resource activities on employee trust. Human Resource Management Review, 7, 389–404]. Despite this, research that has addressed the factors that foster trust in leaders and the outcomes of this trust has been disjointed and, as yet, no comprehensive model has been presented to systematically examine these factors. Therefore, the purpose of this article will be to present an integrative model of trust in leadership.     

Certifiable Pre‐Play Communication: Full Disclosure

This article asks when communication with certifiable information leads to complete information revelation. We consider Bayesian games augmented by a pre‐play communication phase in which announcements are made publicly. We first characterize the augmented games in which there exists a fully revealing sequential equilibrium with extremal beliefs (i.e., any deviation is attributed to a single type of the deviator). Next, we define a class of games for which existence of a fully revealing equilibrium is equivalent to a richness property of the evidence structure. This characterization enables us to provide different sets of sufficient conditions for full information disclosure that encompass and extend all known results in the literature, and are easily applicable. We use these conditions to obtain new insights in games with strategic complementarities, voting with deliberation, and persuasion games with multidimensional types.     

The expected impact of the 2019 Brazilian pension reform on survivors’ pensions

This study analyses the expected changes in survivors’ pensions resulting from the permanent rules of the 2019 pension reform in Brazil. Actuarial annuities are used for representative worker profiles. The dispersion in the replacement rate values decreases, except for the highest income level. The rates needed to finance survivors’ pensions decrease relatively more than do the rates for old-age pensions. The internal rates of return significantly decrease. There is a heterogeneous change in the distributive aspects of the pension system. The reform shall affect the adequacy and intragenerational equity of old-age and survivors’ pensions.     

Explaining Investment Dynamics in U.S. Manufacturing: A Generalized (S,s) Approach

In this paper we derive a model of aggregate investment that builds from the lumpy microeconomic behavior of firms facing stochastic fixed adjustment costs. Instead of the standard sharp (S,s) bands, firms' adjustment policies take the form of a probability of adjustment (adjustment hazard) that responds smoothly to changes in firms' capacity gap. The model has appealing aggregation properties, and yields nonlinear aggregate time series processes. The passivity of normal times is, occasionally, more than offset by the brisk response to large accumulated shocks. Using within and out-of-sample criteria, we find that the model performs substantially better than the standard linear models of investment for postwar sectoral U.S. manufacturing equipment and structures investment data.     

Transform orders and stochastic monotonicity of statistical functionals

In some inferential statistical methods, such as tests and confidence intervals, it is important to describe the stochastic behavior of statistical functionals, aside from their large sample properties. We study such a behavior in terms of the usual stochastic order. For this purpose, we introduce a generalized family of stochastic orders, which is referred to as transform orders, showing that it provides a flexible framework for deriving stochastic monotonicity results. Given that our general definition makes it possible to obtain some well known ordering relations as particular cases, we can easily apply our method to different families of functionals. These include some prominent inequality measures, such as the generalized entropy, the Gini index, and its generalizations. We also illustrate the applicability of our approach by determining the least favorable distribution, and the behavior of some bootstrap statistics, in some goodness-of-fit testing procedures.     

Predicting burrowing owl flight trajectories in urban environments

Urban Ecosystems - It is important to understand how animals respond to changes in landscape structure, especially when considering habitat alteration and urbanization. Using the burrowing owl...