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,

Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts

Simulation methods are used to measure the expected differentials between the Mean Square Errors of the forecasts from models based on temporally disaggregated versus aggregated data. This allows for novel comparisons including long-order ARMA models, such as those expected with weekly data, under realistic conditions where the parameter values have to be estimated. The ambivalence of past empirical evidence on the benefits of disaggregation is addressed by analyzing four different economic time series for which relatively large sample sizes are available. Because of this, a sufficient number of predictions can be considered to obtain conclusive results from out-of-sample forecasting contests. The validity of the conventional method for inferring the order of the aggregated models is revised.Data Aggregation, Efficient Forecasting, Research Methods/ Statistical Methods,