BORROWING BEHAVIOR OF THE PROPRIETARY FIRM: DO SOME RISK-AVERSE EXPECTED UTILITY MAXIMIZERS PLUNGE?

When a proprietor's liability is limited, borrowing behavior for an expected utility maximizer may vary widely. Proprietors with little to lose may rationally choose very large debt levels while others may choose to finance with 100% equity. This article presents a theory to explain these widely observed variations in behavior.Financial Economics,

RISK ANALYSIS FOR PROPRIETORS WITH LIMITED LIABILITY: A MEAN- VARIANCE, SAFETY- FIRST SYNTHESIS

Since nearly the entire U.S. output of agricultural commodities is produced by proprietors with limited liability, it is important to understand how limited liability affects decision in a risky environment. This article extends the work of Robinson and Barry; Robinson and Lev; and Robinson, Barry, and Burghart. It provides a rigorous derivation of one of their objective functions, compares it to standard risk analysis tools, and suggests several methods of empirical implementation. Under some conditions, utility maximization in the limited liability environment is consistent with optimization of Roy's safety-first criterion, while in other situations Freund's mean-variance criterion is appropriate. However, it is easy to demonstrate cases where neither criterion is applicable.Risk and Uncertainty,

Missing observations in multiyear rotation sampling designs

Because Multiyear estimation of at-harvest stratum crop proportions is more efficient than single year estimation, the behavior of multiyear estimators in the presence of missing acquisitions was studied. Only the (worst) case when a segment proportion cannot be estimated for the entire year is considered. The effect of these missing segments on the variance of the at-harvest stratum crop proportion estimator is considered when missing segments are not replaced, and when missing segments are replaced by segments not sampled in previous years. The principle recommendations are to replace missing segments according to some specified strategy, and to use a sequential procedure for selecting a sampling design; i.e., choose an optimal two year design and then, based on the observed two year design after segment losses have been taken into account, choose the best possible three year design having the observed two year parent design

A SIMULATION STUDY OF THE SMALL SAMPLE PROPERTIES OF LIKELIHOOD BASED INFERENCE FOR THE BETA DISTRIBUTION

Researchers often collect proportion data that cannot be interpreted as arising from a set of Bernoulli trials. Analyses based on the beta distribution may be appropriate for such data. The SAS® GLIMMIX procedure provides a tool for these analyses using a likelihood based approach in the context of generalized linear mixed models. Since the t and F-distribution based inference employed in this approach relies on asymptotic properties, it is important to understand the sample sizes required to obtain reasonable approximate answers to inference questions. In addition, the complexity of the likelihood functions can lead to numerical issues for optimization algorithms that may or may not be related to sample size issues. This simulation study is based on a simple intercept-only model for known beta distributed responses. Convergence and estimation issues are investigated over a range of beta distributions and sample sizes

ESTIMATION OF KINETIC PARAMETERS ASSOCIATED WITH NUTRIENT UPTAKE BY AN INTACT PLANT ROOT SYSTEM

Several mechanistic models have been developed for the prediction of nutrient uptake at low concentrations from the soil by a plant root system. Claassen and Barber (1974 Plant Physiology 54, 564-568; 1976 Agronomy Journal 68, 961-964) presented an experimental procedure to obtain data from intact plants to fit an ion depletion curve and used the data in a model which they developed to predict nutrient uptake. Their model assumed that nutrient absorption from the soil solution followed Michaelis-Menten kinetics. In this paper, we develop a stochastic version of the Claassen-Barber model and illustrate its application to the estimation of the kinetic parameters associated with the uptake of potassium by corn plants. The analysis requires the fitting of a nonlinear regression equation which cannot be explicitly expressed in terms of the response variable. The analysis is potentially further complicated by heterogeneity of variance and an autocorrelated error structure

A MARKOV CHAIN MODEL TO ASSESS RESISTANCE OF CATTLE TO HORN FLIES

The horn fly is an economically important external permanent parasite of cattle. As part of a research project focused on alternatives to chemical control of the horn fly, a study was conducted to determine the degree of innate resistance of individual cattle to the horn fly. A fly resistant cow was defined as one whose horn fly counts were in the lower quartile of the weekly fly count distributions for a herd more often than would be expected by chance. A Markov chain model was formulated and a small sample test for fly resistance was developed. The model and procedure are illustrated using data collected on a herd of Charolais cows. Tests of the Markov chain and stationarity assumptions are discussed and applied to the data

SMALL SAMPLE PROPERTIES OF THE TWO INDEPENDENT SAMPLE TEST FOR MEANS FROM BETA DISTRIBUTIONS

Researchers often collect proportion data that cannot be interpreted as arising from a set of Bernoulli trials. Analyses based on the beta distribution may be appropriate for such data. The SAS® GLIMMIX procedure provides a tool for these analyses using a likelihood based approach within the larger context of generalized linear mixed models (GLMM). The small sample behavior of likelihood based tests to compare the means from two independently sampled beta distributions were studied via simulation when the null hypothesis of equal means holds. Two simulation scenarios were defined by equal and unequal sample sizes and equal scale parameters. A third scenario was defined by equal sample sizes and unequal scale parameters. For all three scenarios the values of the common mean μ ranged from 0 and 0.5 and values of the scale parameter ϕ ranged from 0 to 100

How Attitudes of Important Stakeholder Groups Can Influence Effective Water Quality Management

Preliminary results of a survey of Lincoln Lake agricultural and non-agricultural watershed residents as well as water quality regulators/specialists suggests discrepancies exists in different groups perceptions of water quality, the sources of water pollution, and the roles of local, county, state and federal officials in meeting water quality objectives.Resource /Energy Economics and Policy, Q25, Q53, Q59,