Trade theories covering Preferential Trade Agreements (PTAs) are as diverse as the literature in search of their empirical support. To account for the model uncertainty that surrounds the validity of the competing PTA theories, we introduce Bayesian Model Averaging (BMA) to the PTA literature. BMA minimizes the sum of Type I and Type II error, the mean squared error, and generates predictive di...

The behavior of Bayesian model averaging (BMA) for the normal linear regression in presence influential observations that contribute to misfit is investigated. Remedies attenuate potential negative impacts such on inference and prediction are proposed. methodology motivated by view well-behaved residuals good predictive performance often go hand-in-hand. Focus placed models use variants Zellner...

Bayesian model averaging enables one to combine the disparate predictions of a number of models in a coherent fashion, leading to superior predictive performance. The improvement in performance arises from averaging models that make different predictions. In this work, we tap into perhaps the biggest driver of different predictions—different analysts—in order to gain the full benefits of model ...

Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product a...

This paper describes the gretl function package ParMA, which provides Bayesian model averaging (BMA) in generalized linear models. In order to overcome lack of analytical specification for many models covered, features an implementation reversible jump Markov chain Monte Carlo technique, following original idea by Green (1995), as a flexible tool several specifications. Particular attention is ...

Recent approaches to development accounting reflect substantial model uncertainty at both the instrument and the development determinant level. Bayesian Model Averaging (BMA) has been proven useful in resolving model uncertainty in economics, and we extend BMA to formally account for model uncertainty in the presence of endogeneity. The new methodology is shown to be highly efficient and to red...

Abstract Statistical post-processing of ensemble forecasts could effectively improve their accuracy and reliability. In this study, three typical methods including equal weight (EW), model output statistics (MOS) Bayesian averaging (BMA) were applied to the raw multi-model runoff during flood period (from 1 June 30 September) 2010-2013, processed results compared analyzed. It is shown that BMA ...

Ensembles of forecasts are typically employed to account for the forecast uncertainties inherent in predictions of future weather states. However, biases and dispersion errors often present in forecast ensembles require statistical post-processing. Univariate post-processing models such as Bayesian model averaging (BMA) have been successfully applied for various weather quantities. Nonetheless,...