Se tratan los modelos bayesianos biparamétricos para el cálculo de cotas del error total en una auditoría en el contexto de los diferentes modelos estadísticos desarrollados en la literatura contable. En particular, en este artículo se estudia la robustez de la (discutible) hipótesis de...

This paper reports an empirical application of new Baynesian methodology to Australian data on consumption, income, liquid assets and inflation. The methods involve the use of objective model based reference priors and objective posterior odds test criteria. The paper provides an overview of...

This paper provides detailed responses to the following 8 discussants of my paper "To Criticize the Critics: An Objective Bayesian Analysis of Stochastic Trends": Gary Koop and Mark Steel; Edward Leamer; In-Moo Kim and G.S. Maddala Dale J. Poirier; Peter C. Schotman and Herman K. van Dijk; James...

In this paper we have demonstrated the implications of incorrectly normalising the parameters of a reduced rank regression model to achieve global identification, and presented a method for estimating this model without using the ordering restrictions imposed in previous Bayesian and frequentist...

In recent years a new consensus has emerged in macroeconomics in general and in model building in particular, the so called New Keynesian Paradigm (NKM). This paper applies Bayesian estimation techniques to a time series data set of the euro area and presents estimates of a DSGE model. The...

We investigate the persistence of real exchange rates using Bayesian methods. First, an algorithm for Bayesian estimation of nonlinear threshold models is developed. Unlike standard grid-based estimation, the Bayesian approach fully captures joint parameter uncertainty and uncertainty about...

We propose a new approach to imposing economic constraints on time-series forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We...

Bayesian model averaging attempts to combine parameter estimation and model uncertainty in one coherent framework. The choice of prior is then critical. Within an explicit framework of ignorance we define a ‘suitable’ prior as one which leads to a continuous and suitable analog to the...

This article is concerned with the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals which implement, respectively, the exact Bayesian Model Averaging (BMA) estimator and the Weighted Average Least...

Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical growth model, many new (endogenous) growth models have been proposed. This causes a lack of robustness of the parameter estimates and makes the determination of the key determinants of growth...