Computational tools for comparing asymmetric GARCH models via Bayes factors

In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For each model we compute an approximation to the marginal likelihood, based on the MCMC output. From these approximations we compute Bayes factors and posterior model probabilities. (C) 2012 IMACS. Published by Elsevier B.V. All rights reserved.

Perfomance of methods for estimating reference evapotranspiration in the municipalities of Frederico Westphalen and Palmeira das Missoes, State of Rio Grande do Sul, Brazil

The estimation of reference evapotranspiration (ETo), used in water balance, allows to determine soil water content, assisting on irrigation management. The present study aimed to compare simple ETo estimating methods with the Penman-Monteith (FAO), in the folowing time scales: daily, 5, 10, 15 and 30 days and monthly in the counties of Frederico Westphalen and Palmeira das Missoes, in the Rio Grande do Sul state, Brazil. The methods tested had their efficiency improved by increasing the time scale of analysis, keeping the same performance for both locations. The highest and lowest ETo values occurred in December and June, respectively. Most methods underestimated ETo. For any of the time scales Makking and Radiaton FAO24 methods can replace the Penman-Monteith for estimating ETo.

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.