We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general...

Introduction: Common approaches in cost-effectiveness analyses do not adjust for confounders. In nonrandomized studies this can result in biased results. Parametric models such as regression models are commonly applied to adjust for confounding, but there are several issues which need to be...

Explained variance (R^2) is a familiar summary of the fit of a linear regression and has been generalized in various ways to multilevel (hierarchical) models. The multilevel models we consider in this paper are characterized by hierarchical data structures in which individuals are grouped into...

Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral- t family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative...

This paper extends the methods developed by Hamilton (1989) and Chib (1996) to identified multiple-equation models. It details how to obtain Bayesian estimation and inference for a class of models with different degrees of time variation and discusses both analytical and computational difficulties

This paper provides a survey and synthesis of econometric tools that have been employed to study economic growth. While these tools range across a variety of statistical methods, they are united in the common goals of first, identifying interesting contemporaneous patterns in growth data and...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag model is chosen. The results show that a sharp...

We investigate the factor exposure of smart beta ETFs under model uncertainty using Bayesian variable selection. We find that smart beta ETFs have exposures to several factors, including size, value, momentum, quality, volatility/low beta, and dividend yield. The average return contribution of...

This paper examines the nexus between financial development and economic growth in Ghana between 1970 and 2020. The study adopted the Bayesian Model Averaging (BMA) techniques to address issues of model uncertainty due to many potential explanatory variables that could influence growth. The...

Introduction: Common approaches in cost-effectiveness analyses do not adjust for confounders. In nonrandomized studies this can result in biased results. Parametric models such as regression models are commonly applied to adjust for confounding, but there are several issues which need to be...