Terry L. Kastens

Model Selection and Forecasting Ability of Theory-Constrained Food Demand Systems

Out-of-sample forecasting of annual U.S. per capita food consumption, applying data from 1923 to 1992, is used as a basis for model selection among the absolute price Rotterdam model, a first-differenced linear approximate almost ideal demand system (FDLA/ALIDS) model, and a first-differenced double-log demand system. Conditional-on-price consumption forecasts derived from elasticities are determined to be superior to direct statistical model forecasts. Models with consumer theory imposed through parametric restrictions provide better forecasts than models with little theory-imposition. For these data, a double-log demand system is a superior forecaster to the Rotterdam model, which is superior to the FDLA/ALIDS model. Copyright 1996, Oxford University Press.

Evaluating the semi-nonparametric fourier, aim, and neural networks cost functions

Abstract This study compares how well three semi-nonparametric functions, the Fourier flexible form, asymptotically ideal model, and neural networks, approximate simulated production data. Results show that higher order series expansions better approximate the true technology for data sets that have little or no measurement error. For highly nonlinear technologies and much measurement error, lower order expansions may be appropriate.

Payoffs to Farm Management: How Important is Crop Marketing?

In production agriculture, good management is demonstrated by profits that are persistenly greater than those of similar neighboring farms. This research examined the effects of management practices on risk-adjusted profit per acre for Kansas farms over 1990-1999. The management practices were price, cost, yield, planting intensity, and technology adoption (less-tillage). Cost management, planting intensity, and technology adoption had the greatest effect on profit per acre, and cash price management was found to have the smallest impact. If producers wish to have continuously high profits, their efforts are best spent in management practices over which they have the most control.

Does Market Timing Contribute to the Cattle Cycle

Recent evidence suggests that cyclical cattle inventories are driven by exogenous shocks. This article examines a second possible contributing factor to the cattle cycle: a market timing effect that arises from individual attempts to maintain countercyclical inventories. The model uncovers an important conceptual point: to the extent that cycles are driven by exogenous shocks, a representative producer should outperform one who maintains a constant inventory; whereas, for cycles induced by market timing, a representative producer should underperform one with a constant inventory. Simulated net returns over 1974−98 reveal that a constant-inventory manager significantly outperformed the representative U.S. producer, which indicates that market timing influences the cattle cycle. Copyright 2000, Oxford University Press.

The Motivation for Organic Grain Farming in the United States: Profits, Lifestyle, or the Environment?

The objective of this research is to identify and quantify the motivations for organic grain farming in the United States. Survey data of US organic grain producers were used in regression models to find the statistical determinants of three motivations for organic grain production, including profit maximization, environmental stewardship, and an organic lifestyle. Results provide evidence that many organic grain producers had more than a single motivation and that younger farmers are more likely to be motivated by environmental and lifestyle goals than older farmers. Organic grain producers exhibited a diversity of motivations, including profit and stewardship.

PER UNIT COSTS TO OWN AND OPERATE FARM MACHINERY

With increasingly thin margins and new technologies, it is important that farm managers know their cost of field operations on a per unit basis (e.g., acre, ton, bale). Accurate per unit costs give confidence when constructing enterprise budgets and evaluating new technologies, such as no-till. Custom rates are often used as a proxy for per unit costs; however, this research, using entropy and jackknife estimation procedures, found that custom rates understate total ownership and operating costs by approximately 25% for an average Kansas farm. Estimates from these models are then used to benchmark actual costs against expected cost.

Forecast Performance of Neural Networks and Business Cycle Asymmetries

Forecast performance of artificial neural network models are investigated using Ashley et al. (1980) and the neural network nonlinearity test proposed by Lee et al. (1993) is employed to find possible existence of business cycle asymmetries in Canada, France, Japan, UK and USA real GDP growth rates. The results show that neural network models are more accurate than linear models for in-sample forecasts. However, when comparing the out-of-sample, linear models performed better than neural network models in all series. Results from neural network tests show that business cycle asymmetries do prevail in all the series.

Improved prospects for rural development: an industrial targeting system for the Great Plains

This research introduces an industrial targeting system intended to improve local decision making related to selection of targets for business recruitment. The Plains Economic Targeting System (PETS) consists of a series of econometric equations that match industry input and market requirements with community characteristics to generate a probability of new business location over a given time period. The system matches location requirements for 78 industry sectors to local characteristics for 414 counties in six Great Plains states. Further, the coefficients generated for a given county are transformed into marginal impacts, providing important information relating to local policies that can improve the probability of attracting a given industry.

Non-Parametric and Semi-Parametric Techniques for Modeling and Simulating Correlated, Non-Normal Price and Yield Distributions: Applications to Risk Analysis in Kansas Agriculture

Parametric, non-parametric, and semi-parametric approaches are commonly used for modeling correlated distributions. Semi-parametric and non-parametric approaches are used to examine the risk situation for Kansas agriculture. Results from the model indicate that 2000 will be another difficult year for Kansas farmers, although crop income will increase slightly from 1999. However, unless another supplemental infusion of government payments occurs, crop income is expected to be the lowest since 1992.

Predicting Farm Tractor Values through Alternative Depreciation Methods

This study compares a variety of farm tractor depreciation methods to determine which most accurately estimates farm tractor values. These alternative depreciation methods consider different factors for estimating remaining value and vary in difficulty of use. Pairwise comparisons of mean absolute percentage error and forecast accuracy regression models were used to evaluate the accuracy of the depreciation methods, which depend on age, intensity of use, and manufacturer. Based on the results of this study, the Cross and Perry method was generally the most accurate. Copyright 2003, Oxford University Press.