Ardian Harri

The Relationship between Oil, Exchange Rates, and Commodity Prices

Exchange rates have long been thought to have an important impact on the export and import of goods and services, and, thus, exchange rates are expected to influence the price of those products that are traded. At the same time, energy impacts commodity production in some very important ways. The use of chemical and petroleum derived inputs has increased in agriculture over time; the prices of these critical inputs, then, would be expected to alter supply, and, therefore, the prices of commodities using these inputs. Also, agricultural commodities have been increasingly used to produce energy, thereby leading to an expectation of a linkage between energy and commodity markets. In this paper, we examine the price relationship through time of the primary agricultural commodities, exchange rates, and oil prices. Using overlapping time periods, we examine the cointegration relationship between prices to determine changes in the strength of the linkage between markets through time. In general, we find that commodity prices are linked to oil for corn, cotton, and soybeans, but not for wheat, and that exchange rates do play a role in the linkage of prices over time.

More than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields

The objective of this article is to propose the use of moment functions and maximum entropy techniques as a flexible way to estimate conditional crop yield distributions. We present a moment based model that extends previous approaches in several dimensions, and can be easily estimated using standard econometric estimators. Upon identification of the yield moments under a variety of climate and irrigation regimes, we utilize maximum entropy techniques to analyze the distributional impacts from switching regimes. We consider the case of Arkansas, Mississippi, and Texas upland cotton to demonstrate how climate and irrigation affect the shape of the yield distribution, and compare our findings to other moment based approaches. We empirically illustrate several advantages of our moment based maximum entropy approach, including flexibility of the distributional tails across alternative irrigation and climate regimes.

Relaxing Heteroscedasticity Assumptions in Area-Yield Crop Insurance Rating

This article focuses on the effect of differing heteroscedasticity assumptions on derived premium rates of area-yield crop insurance. Tests of the proportional and absolute heteroscedasticity assumptions are conducted using both in-sample and out-of-sample measures. Our results suggest that arbitrarily imposing a specific form of heteroscedasticity or homoscedasticity in insurance rate calculations limits actuarial soundness. Our results have practical implications for the federal crop insurance programs, as we reject the traditional rating assumptions for many cotton regions and lower-yielding/higher-risk corn and soybean counties but not in the heart of the Cornbelt. Copyright 2011, Oxford University Press.

Crop Supply Response under Risk: Impacts of Emerging Issues on Southeastern U.S. Agriculture

In this paper we consider factors that affect both crop prices and yields in order to examine supply responses of major crops in the Southeast. Due to the variable nature of crop production in the Southeast, previous studies that ignore price and yield risk may fail to capture one of the salient features of the region’s agriculture. Our results indicate supply elasticity values for corn, cotton, and soybeans of approximately 0.670, 0.506, and 0.195, respectively. Compared with the results of studies in other regions, corn and cotton acres respond more to price changes and soybean acres respond less to price changes.

Who Buys Food Directly from Producers in the Southeastern United States

To capitalize on potential opportunities presented by growing consumer demand for locally grown foods, farmers need insight into significant motivations and behavioral characteristics of consumers in their region. This article aims to evaluate the characteristics of southeastern urban consumers who purchased food directly from producers. Novel study findings include the impact of disease incidences that occurred in respondent and related family members, a more accurate understanding of U.S. agriculture, relatively higher levels of concern about U.S. food safety, and greater physical activity levels, which are significant motivators of increased likelihood to purchase direct from producers.

Spatial Pattern of Yield Distributions: Implications for Crop Insurance

Crop insurance is similar to flood and hurricane insurance in that spatially correlated weather tends to cause violations of the independence assumption. Ideally, one would seek to pool uncorrelated risk drawn from the same distribution in crop insurance. This article proposes a testing procedure for the cross-sectional pooling of group units, and empirically analyzes whether the proposed test improves out-of-sample rating performance. We utilize a balanced panel of U.S. county-level corn yields for 510 counties, and the results of an out-of-sample crop insurance rating performance exercise provide economic significance to the proposed pooling methodology and results.

Normality testing: two new tests using L-moments

Establishing that there is no compelling evidence that some population is not normally distributed is fundamental to many statistical inferences, and numerous approaches to testing the null hypothesis of normality have been proposed. Fundamentally, the power of a test depends on which specific deviation from normality may be presented in a distribution. Knowledge of the potential nature of deviation from normality should reasonably guide the researchers selection of testing for non-normality. In most settings, little is known aside from the data available for analysis, so that selection of a test based on general applicability is typically necessary. This research proposes and reports the power of two new tests of normality. One of the new tests is a version of the R-test that uses the L-moments, respectively, L-skewness and L-kurtosis and the other test is based on normalizing transformations of L-skewness and L-kurtosis. Both tests have high power relative to alternatives. The test based on normalized transformations, in particular, shows consistently high power and outperforms other normality tests against a variety of distributions.

Producer preferences for contracts on a risky bioenergy crop

This study employs a stated choice experiment survey to identify producer preferences for contracts to produce Giant Miscanthus. Preliminary results indicate that price offered per ton of harvested Miscanthus, yield insurance availability, and biorefinery harvest have significant positive effects on the probability of a producer accepting a contract to produce Giant Miscahthus. The results show that risk-neutral farmers as more willing to accept contracts relative to risk-loving farmers, ceteris paribus. Farmers who perceive yield risk of Miscathus to be greater than their current crop are less likely to accept Giant Miscanthus contracts.

Estimating a Demand System with Seasonally Differenced Data

Researchers estimating demand systems have often used annual data even though monthly or quarterly data are available. Monthly data may be avoided because with monthly data it becomes more difficult to specify seasonality, autocorrelation is more likely to be significant, and there is a greater chance of finding significant dynamics in demand. This paper shows how to obtain consistent and asymptotically efficient estimates of a demand system using seasonal differenced data. It also shows that several alternative estimators are either inefficient or implausible for demand systems.

Using Bayesian Kriging for Spatial Smoothing in Crop Insurance Rating

Abstract Rating insurance policies depends on the probability of events in the tail of a distribution. A method to measure such tail‐related risk based on Extreme Value Theory could potentially improve insurance rating. It is also widely agreed that there is a spatial structure to crop yield distributions. Considering the spatial structure may provide more precisely rated policies. In this context, this research provides two contributions in rating area yield crop insurance. One is to provide a method that fits the tail of crop yield distributions using the Generalized Pareto Distribution (GPD), a member of the family of extreme value distributions that models only the tail of the distribution. The second is to estimate parameters of the distribution using a Bayesian Kriging approach that provides spatial smoothing of GPD parameters. The proposed model provides estimates of the spatial structure across regions such as the maximum distance of the spatial effect. Based on an out‐of‐sample performance game between a private insurance company and the federal agency the proposed model provides considerable improvement, particularly when rating deeper tail probability.