This paper makes use of Hierarchical Bayes Models to model and estimate spatial health effects. We focus on Germany, combining rich individual-level household panel data with administrative county level information to estimate spatial county-level health dependencies. As dependent variable, we...

This paper uses Hierarchical Bayes Models to model and estimate spatial health effects in Germany. We combine rich individual-level household panel data from the German SOEP with administrative county-level data to estimate spatial county-level health dependencies. As dependent variable we use...

Social interactions are generally thought to play an important role in smoking initiation among adolescents. In this paper we exploit detailed friendship nominations in the US Add Health data, and extend the Spatial Autoregressive Model (SAR) model to deal with (i) endogenous peer selection, and...

Social interactions are widely recognized to play an important role in smoking initiation among adolescents. In this paper, we hypothesize that emotionally stable, conscientious individuals are better able to resist peer pressure in the uptake of smoking. We exploit detailed friendship...

This tutorial is designed to introduce readers to Bayesian variants of the standard SAR and SEM models that are the most widely used and applied models in spatial econometrics. Particular attention is paid to the mathematical derivations required to obtain the full conditional distributions...

This article describes an R package bqror that estimates Bayesian quantile regression for ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers computationally efficient, yet simple, MCMC algorithms for estimating ordinal quantile regression....

In many manuscripts, researchers use multivariable logistic regression to adjust for potential confounding variables when estimating a direct relationship of a treatment or exposure on a binary outcome. After choosing how variables are entered into that model, researchers can calculate an...

This study proposes a Bayesian approach for exact finite-sample inference of an instrument-free estimation method that builds upon joint estimation using copulas to deal with endogenous covariates. Although copula approaches with applications to handle regressor-endogeneity have been frequently...

While demand models require a sound understanding of economic processes and should be flexible enough to capture nonlinearities, endogeneity can greatly hinder the identification of (nonlinear) causal relationships. To tackle these issues, we extend the instrument-free Gaussian copula approach...

Total Factor Productivity (TFP) accounts for a sizeable proportion of the income and growth differences across countries. Two challenges remain to researchers aiming to explain these differences: on the one hand, TFP growth is hard to measure; on the other hand, model uncertainty hampers...