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,

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,

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,

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,

ASSESSING THE FINANCIAL RISKS OF DIVERSIFIED COFFEE PRODUCTION SYSTEMS: AN ALTERNATIVE NONNORMAL CDF ESTIMATION APPROACH

Recently developed techniques are adapted and combined for the modeling and simulation of crop yields and prices that can be mutually correlated, exhibit heteroskedasticity or autocorrelation, and follow nonnormal probability density functions. The techniques are applied to the modeling and simulation of probability distribution functions for the returns of three tropical agroforestry systems for coffee production. The importance of using distribution functions that can more closely reflect the statistical behavior of yields and prices for risk analysis is discussed and illustrated.Risk and Uncertainty,

JOINT MODELING AND SIMULATION OF AUTOCORRELATED NON-NORMAL TIME SERIES: AN APPLICATION TO RISK AND RETURN ANALYSIS

This study presents a technique that can jointly model and simulate the expected values, variances, and covariances of sets of correlated time-series dependent variables that are autocorrelated and non-normal (right or left skewed and/or kurtotic). It illustrates the method by applying it to risk analysis of diversified tropical agroforestry systems.Resource /Energy Economics and Policy, Risk and Uncertainty,

RISK AND RETURNS OF DIVERSIFIED CROPPING SYSTEMS UNDER NONNORMAL, CROSS-, AND AUTOCORRELATED COMMODITY PRICE STRUCTURES

This study analyzes the risks of diversified tropical cropping systems that combine cocoa, plantain, and tree-crop components in different proportions versus traditional monocultures. A technique for modeling the expected values, variances, and covariances of correlated time-series variables that are autocorrelated and nonnormal (right or left skewed and kurtotic) is applied to simulate commodity prices. The importance of using simulated cumulative density functions (cdf's) which reflect the most important characteristics of the stochastic behavior of prices for analyzing risk and returns of diversified agricultural systems is demonstrated. The analysis priovides evidence in favor of diversified cocoa-plantain-Cordia agroforestry system technologies versus the traditional monocultures.Demand and Price Analysis,

Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models

The performance of a proposed asymmetric-error GARCH model is evaluated in comparison to the normal-error- and Student-t-GARCH models through three applications involving forecasts of U.S. soybean, sorghum, and wheat prices. The applications illustrate the relative advantages of the proposed model specification when the error term is asymmetrically distributed, and provide improved probabilistic forecasts for the prices of these commodities.GARCH, nonnormality, skewness, time-series forecasting, U.S. commodity prices, Demand and Price Analysis,

An Empirically-Grounded Comparison of the Johnson System versus the Beta as Crop Yield Distribution Models

Previous research established that the expanded Johnson system can accommodate any theoretically possible mean-variance-skewness-kurtosis combination. Therefore, it has been hypothesized that this system can provide for a reasonably accurate modeling approximation of any probability distribution that might be encountered in practice. In order to test that hypothesis, this manuscript develops a more flexible expanded form of the Beta distribution which, in its original form, has been widely used to model and simulate crop yields for risk analysis. Empirically grounded evaluations suggest that the Johnson system can model a variety of typical yield data-generating processes that are based on the Beta distribution much more precisely than the Beta can model representative crop yield data simulated from the Johnson system. The accuracy with which the Johnson system approximates the Beta supports the previously stated hypothesis.Crop Production/Industries,

Premium Estimation Inaccuracy and the Actuarial Performance of the US Crop Insurance Program

This article explores the impact of the likely levels of inaccuracy associated with two main types of premium estimation methods, under different sample sizes, on the actuarial performance of the US crop insurance program. The analyses are conducted under several plausible assumptions about the insurer versus the producers’ estimates for their actuarially fair premiums. Significant differences are found due to estimation method and sample size, with the currently used procedures resulting in the worse actuarial performance. Several conclusions and recommendations are provided that could markedly reduce the amount of public subsidies needed to keep this program solvent.Agricultural Subsidies, Crop Insurance Premium Estimation, Loss-Cost Procedures, Risk Management Agency, Financial Economics,