AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICY UNDER ERROR-TERM NON-NORMALITY

This paper explores the impact of error-term non-normality on the performance of the normal-error Generalized Autoregressive Conditional Heteroskedastic (GARCH) model under small and moderate sample sizes. A non-normal-, asymmetric-error GARCH model is proposed, and its finite-sample performance is evaluated in comparison to the normal-error GARCH under various underlying error-term distributions. The results suggest that one must be skeptical of using the normal-error GARCH when there is evidence of conditional error-term non-normality. The conditional distribution of the error-term in a previous mainstream application of the normal GARCH is found to be non-normal and asymmetric. The same application is used to illustrate the advantages of the proposed non-normal-error GARCH model.Error- term non-normality, skewness, autoregressive conditional heteroskedasticity, Research Methods/ Statistical Methods,

USE OF ASYMMETRIC-CYCLE AUTOREGRESSIVE MODELS TO IMPROVE FORECASTING OF AGRICULTURAL TIME SERIES VARIABLES

Threshold autoregressive (TAR) models can accommodate the asymmetric cycling behavior observed in some time series data. This study develops a procedure to estimate TAR models when the conditional mean of the dependent variable is function of one or more exogenous factors while allowing for heteroskedasticity, i.e. for different levels of variation in upward versus downward cycles. The formulas to obtain predictions from TAR models are derived. Monte Carlo simulation analyses suggest that TAR models can significantly improve forecasting precision. Substantial gains in forecasting precision, in comparison with AR models, are in fact found when applying the proposed procedure to the modeling of U.S. quarterly soybeans future prices and Brazilian coffee spot prices. The estimated TAR models also provide useful insights on the markedly different dynamics of the upward versus the downward cycles exhibited by U.S. soybeans and Brazilian coffee prices.Research Methods/ Statistical Methods,

Dyes removal from water using low cost absorbents

In this study, the removal capacity of low cost adsorbents during the adsorption of Methylene Blue (MB) and Congo Red (CR) at different concentrations (50 and 100mg•L-1) was evaluated. These adsorbents were produced from wood wastes (cedar and teak) by chemical activation (ZnCl2). Both studied materials, Activated Cedar (AC) and activated teak (AT) showed a good fit of their experimental data to the pseudo second order kinetic model and Langmuir isotherms. The maximum adsorption capacities for AC were 2000.0 and 444.4mg•g-1 for MB and CR, respectively, while for AT, maximum adsorption capacities of 1052.6 and 86.4mg•g-1 were found for MB and CR, respectively. © Published under licence by IOP Publishing Ltd

PARAMETRIC MODELING AND SIMULATION OF JOINT PRICE-PRODUCTION DISTRIBUTIONS UNDER NON-NORMALITY, AUTOCORRELATION AND HETEROSCEDASTICITY: A TOOL FOR ASSESSING RISK IN AGRICULTURE

This study presents a way to parametrically model and simulate multivariate distributions under potential non-normality, autocorrelation and heteroscedasticity and illustrates its application to agricultural risk analysis. Specifically, the joint probability distribution (pdf) for West Texas irrigated cotton, corn, sorghum, and wheat production and prices is estimated and applied to evaluate the changes in the risk and returns of agricultural production in the region resulting from observed and predicted price and production trends. The estimated pdf allows for time trends on the mean and the variance and varying degrees of autocorrelation and non-normality (kurtosis and right- or left-skewness) in each of the price and production variables. It also allows for any possible price-price, production-production, or price-production correlation.agricultural risk analysis, autocorrelation, heteroscedasticity, multivariate non-normal simulation, West Texas agriculture, Research Methods/ Statistical Methods, Risk and Uncertainty,