Rod F. Ziemer

Stochastic Efficiency, Normality, and Sampling Errors in Agricultural Risk Analysis

This paper examines the power of tests for efficiency. The relationship between sample size, parameter values, and the family of probability distributions is stressed. Some findings are that the probability of correctly ranking distributions is frequently very low regardless of sample size. It is generally lowest as distributional parameters (such as the means) of the two distributions being compared are of similar magnitudes. Further, the empirical distribution function performs extremely well as compared to maximum likelihood estimators.

A Stein-rule method for pooling data.

Abstract In this paper a Stein-rule estimator is discussed as an alternative to the traditional statistical approaches to pooling time-series and cross-section data. Ex post forecasts of the Stein-rule applied to a wilderness recreation demand model are compared to that of conventional estimators.

ASSESSMENT OF JOURNALS USED BY AGRICULTURAL ECONOMISTS AT LAND-GRANT UNIVERSITIES

Agricultural economists at land-grant universities were surveyed to evaluate the use and assessment of professional journals. Faculty rankings of journals are reported along with faculty perceptions of changes in the quality of selected journals. Of 25 journals used by agricultural economics faculties, the Southern Journal of Agricultural Economics ranked first among regional agricultural economics journals in personal usefulness, subscriptions held, papers submitted, papers published, and participation in the editorial and review processes. The SJAE was also ranked as the second most improved journal among all journals evaluated.

Farm Real Estate Pricing under Risk: An Empirical Investigation

The rapid increase in farm real estate prices during the last three decades has heightened interest among agricultural economists in explaining this phenomenon. Even prior to the 1950s, several researchers expressed concern that farm real estate prices were increasing at a faster rate than could be justified by farm income [29]. Technological change and government programs were evaluated for structural changes in price determination [6; 13]. Other important explanatory variables were farm enlargement and urbanization influences [34]. More recently, Harris and Nehring [11], Lee and Rask [18], Harris [10], and Boehlje and Griffin [3] explored the effects of such factors as farm size, inflation, and government price supports on farm real estate prices. While previous research contributed to understanding farm real estate price determination, several important questions remain unanswered. How is the farm real estate market linked to other capital markets? Heady and Tweeten [12] and later Reynolds and Timmons [30] used rate of return on common stock as an explanatory variable in their farm real estate price models. However, their approaches are based more on intuitive reasoning than on rigorous theoretical development. How does risk affect farm real estate prices? The fact that real estate is a risky investment generally was ignored in previous research [2, 441-4]. Recently, Harris [10] and Boehlje and Griffin [3] included risk information in their analyses of factors affecting bid prices for land. These studies use strong microfoundations that are not intended to directly reflect risk-related linkages with other markets or sectors. These issues can be addressed conceptually through capital market theory. The purpose of this paper is to explore farm real estate price determination in the context of capital market theory to determine the relationship between the farm real estate

Small sample performance of the Stein-Rule in non-orthogonal designs

Abstract The effects of multicollinearity upon the risk improvement provided by Stein-like estimators over the maximum likelihood estimator in the normal regression model are investigated via Monte Carlo methods. Risk gains are found to degenerate rapidly given moderate degrees of multicollinearity.

An application of a truncated regression model to recreation demand.

The problems associated with limited dependent variables in recreation demand models have been generally ignored in previous research efforts. A probable reason for not considering these problems is that practical procedures to test for truncation bias have only recently become available. A method suggested by Olsen can serve as an indicator regarding the severity of possible sample truncation bias inherent in OLS estimates of recreation demand parameters. This paper illustrates Olsens method applied to wilderness recreation demand models. The results illustrate that a simple transformation of parameter estimates assocated with OLS can be used to adjust for a sample truncation problem.

Tolerance Limits and Expected Utility Analysis

Virtually all applied work in the economics of uncertainty has used the expected utility framework (Machina). In agricultural economics, expected utility analysis has been applied in many studies involving agricultural production (Anderson), pest management strategies (Musser, Tew, and Epperson), rural bank portfolio behavior (Robison and Barry), and farm policy (Kramer and Pope). These empirical analyses follow one of two general approaches. The first is comparison of expected value and variance of net returns from alternative risky prospects and is usually referred to as meanvariance or EV analysis. The second, stochastic dominance (or efficiency) analysis, involves comparing probability distributions of net returns (or other monetary outcomes) from risky decision alternatives. In both, the underlying probability distribution for net returns is estimated either directly (stochastic efficiency) or implicitly (EV analysis). Risky alternatives are then compared on the basis of the expected utility they generate. In this paper, we determine the degree of confidence, in a probabilistic sense, that can be placed in estimated distribution functions used to estimate expected utility and order risky prospects. Tolerance limits are defined and used to show how an investigator can determine the probability that a specified proportion of the underlying probability density is within a particular range of the sample data. Tolerance limits for the entire distribution are presented for specific sample sizes. These values can be used to measure the statistical confidence one can have in estimated distribution functions for expected utility analysis. In some circumstances, we can be reasonably confident that results based on estimated distribution functions will actually hold for the unknown, underlying population distributions.

Research Productivity and Concentration Among Agricultural Economics Faculty in the Northeast

Research activities of academic agricultural economists in the Northeast Region are examined. Selected categories of research output are presented. Interregional comparisons in research productivity are made between: 1) faculty employed in the Northeast and those employed elsewhere and 2) faculty educated in the Northeast and those educated elsewhere. Intraregional comparisons of faculty employed in the Northeast by region of education are also presented. Regional differences in research resources and concentration are examined and offered as factors contributing to research productivity differences. Results indicate few significant long-run differences in research productivity and concentration in the Northeast relative to that in other regions.

A Stein-rule approach to outlying observations in regression analysis

In this paper, a Stein-rule procedure is presented as a minimax alternative to discarding suspected outlying observations in linear regression analysis. The procedure is demonstrated in an empirical example.

Faculty Advisors and Advising Programs in Agricultural Economics Departments in the Northeast

This paper summarizes selected findings of a study of faculty advisors and advising programs in departments of agricultural and resource economics. Undergraduate advising program characteristics in the Northeast are contrasted with those in other regions. Interdepartmental advising loads, advising budgets and allocation of advising resources are measured. Differences were found in advisor selection, training, support, coordination, rewards and evaluations. Advising programs were generally strong in advisor accessability and weak in career follow-ups. Advising faculty members in the Northeast generally earned lower salaries and taught more terms during the year than nonadvising faculty members. Continued program documentation, support and rewards are recommended.