Structural learning and estimation of joint causal effects among network-dependent variables

Statistical Methods & Applications - Bayesian networks in the form of Directed Acyclic Graphs (DAGs) represent an effective tool for modeling and inferring dependence relations among variables,...     

Public Sector Strategies in Curbing Corruption: A Review of the Literature

Public Organization Review - Corruption is widespread and preventive strategies to reduce corruption need to be adapted within the local context. Considering the United Nations (UN) Convention...     

On the finite-sample properties of conditional empirical likelihood estimators

We provide Monte Carlo evidence on the finite-sample behavior of the conditional empirical likelihood (CEL) estimator of Kitamura, Tripathi, and Ahn and the conditional Euclidean empirical likelihood (CEEL) estimator of Antoine, Bonnal, and Renault in the context of a heteroscedastic linear model with an endogenous regressor. We compare these estimators with three heteroscedasticity-consistent instrument-based estimators and the Donald, Imbens, and Newey estimator in terms of various performance measures. Our results suggest that the CEL and CEEL with fixed bandwidths may suffer from the no-moment problem, similarly to the unconditional generalized empirical likelihood estimators studied by Guggenberger. We also study the CEL and CEEL estimators with automatic bandwidths selected through cross-validation. We do not find evidence that these suffer from the no-moment problem. When the instruments are weak, we find CEL and CEEL to have finite-sample properties—in terms of mean squared error and coverage probability of confidence intervals—poorer than the heteroscedasticity-consistent Fuller (HFUL) estimator. In the strong instruments case, the CEL and CEEL estimators with automatic bandwidths tend to outperform HFUL in terms of mean squared error, while the reverse holds in terms of the coverage probability, although the differences in numerical performance are rather small.     

Describing extra-binomial variation with partially correlated models

Data in the form of proportions are often analyzed under a binomial model. However, because genuine random sampling is often infeasible, the subjects in the sample may be collected in clumps and the variances of the observed proportions may be considerably larger than those corresponding to the binomial model. A set of data from a study of the proportion of subjects testing positive to the disease toxoplasmosis is used in this article to motivate partially correlated binomial models capable of describing data observed in practical situations where clumped sampling is likely to appear, According to these models, the extra-binomial variance of the observed frequencies may range from a linear to a quadratic function of the sample size. An efficient algorithm for the evaluation of the resulting probability mass function is given.