Using a survey conduct with 240 Economics students of the University of Brasília in August, 2011, this paper explores the determinants of the academic outcome, measured as the Gross Point Average of the University. The econometric method used to estimate is Ordinary Least Squares with Bayesian...

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 (ADL) model is chosen. The results show that a...

The purpose of this paper is twofold: 1) to highlight the widely ignored but fundamental problem of ‘superpopulations’ for the use of inferential statistics in development studies. We do not to dwell on this problem however as it has been sufficiently discussed in older papers by...

The relationship between calorie and nutrient (fat, protein, and carbohydrates) intake as a function of income is explored using data for 171 countries over two time periods 1990-1992 and 2003-2005. Three types of analysis are employed: i) nonparametric, ii) panel regressions, and iii) quantile...

A debate on the regional disparity is always an interesting topic. This study analysed the regional income disparity in India during 1980 – 2010, which contains pre, early and later reform periods. The study used per capita GSDP data from Central Statistical Organisation. First, the study...

There is a vast literature that specifies Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The...

This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling...

One advantage of Bayesian estimation is its solid theoretical ground on model comparison, which relies heavily upon the accurate calculation of marginal likelihood. The Gelfand-Dey (1994) and Chib (1995) methods are two popular means of calculating marginal likelihood. A trade-off exists between...

This paper addresses the issue of improving the forecasting performance of vector autoregressions (VARs) when the set of available predictors is inconveniently large to handle with methods and diagnostics used in traditional small scale models. First, available information from a large dataset...

This paper develops methods for automatic selection of variables in forecasting Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic (linear and nonlinear) VARs. The performance...