Modeling uncertainty in macroeconomic growth determinants using Gaussian graphical models

Model uncertainty has become a central focus of policy discussion surrounding the determinants of economic growth. Over 140 regressors have been employed in growth empirics due to the proliferation of several new growth theories in the past two decades. Recently Bayesian model averaging (BMA) has been employed to address model uncertainty and to provide clear policy implications by identifying robust growth determinants. The BMA approaches were, however, limited to linear regression models that abstract from possible dependencies embedded in the covariance structures of growth determinants. The recent empirical growth literature has developed jointness measures to highlight such dependencies. We address model uncertainty and covariate dependencies in a comprehensive Bayesian framework that allows for structural learning in linear regressions and Gaussian graphical models. A common prior specification across the entire comprehensive framework provides consistency. Gaussian graphical models allow for a principled analysis of dependency structures, which allows us to generate a much more parsimonious set of fundamental growth determinants. Our empirics are based on a prominent growth dataset with 41 potential economic factors that has been utilized in numerous previous analyses to account for model uncertainty as well as jointness.     

Preserving confidentiality of high-dimensional tabulated data: Statistical and computational issues

Dissemination of information derived from large contingency tables formed from confidential data is a major responsibility of statistical agencies. In this paper we present solutions to several computational and algorithmic problems that arise in the dissemination of cross-tabulations (marginal sub-tables) from a single underlying table. These include data structures that exploit sparsity to support efficient computation of marginals and algorithms such as iterative proportional fitting, as well as a generalized form of the shuttle algorithm that computes sharp bounds on (small, confidentiality threatening) cells in the full table from arbitrary sets of released marginals. We give examples illustrating the techniques.     

The mode oriented stochastic search (MOSS) algorithm for log-linear models with conjugate priors

We describe a novel stochastic search algorithm for rapidly identifying regions of high posterior probability in the space of decomposable, graphical and hierarchical log-linear models. Our approach is based on the Diaconis–Ylvisaker conjugate prior for log-linear parameters. We discuss the computation of Bayes factors through Laplace approximations and the Bayesian iterative proportional fitting algorithm for sampling model parameters. We use our model determination approach in a sparse eight-way contingency table.