LOOKING FOR GOVERNMENT'S ROLE AS AN AGRICULTURAL SAFETY NET

What makes agriculture especially deserving of an active government safety net? What is different about agricultural production? Are we concerned about a safe and reliable food supply or about farmers' incomes and returns to assets? Those who make farm policy base their arguments on all of these points: the diffuse nature of agricultural production, the inherent production risk in agriculture, the need for a safe and reliable food supply, shortcomings in farm income, and low returns to assets in agriculture. This paper addresses these points and, in so doing, rules out some of these concerns as legitimate bases for current agricultural policies. By focusing on those that are genuine, U.S. farm policy could spend limited resources in areas where the most good could be done, thus benefiting both farmers and taxpayers.agricultural policy, farm income, farm-sector safety net, market power, Agricultural and Food Policy, Agricultural Finance,

Do Farmers Hedge Optimally or by Habit? A Bayesian Partial-Adjustment Model of Farmer Hedging

Hedging is one of the most important risk management decisions that farmers make and has a potentially large role in the level of profit eventually earned from farming. Using panel data from a survey of Georgia farmers that recorded their hedging decisions for four years on three crops we examine the role of habit, demographics, farm characteristics, and information sources on the hedging decisions made by 106 different farmers. We find that the role of habit varies widely. Information sources are shown to have significant and large effects on the chosen hedge ratios. The farmer's education level, attitude toward technology adoption, farm profitability, and the ratio of acres owned to acres farmed also play important roles in hedging decisions.Bayesian econometrics, hedging decisions, habit formation, information sources, Agricultural Finance,

Do Farmers Hedge Optimally or by Habit? A Bayesian Partial-Adjustment Model of Farmer Hedging

Hedging is one of the most important risk management decisions that farmers make and has a potentially large role in the level of profit eventually earned from farming. Using panel data from a survey of Georgia farmers that recorded their hedging decisions for 4 years on four crops, we examine the role of habit, demographics, farm characteristics, and information sources on the hedging decisions made by 57 different farmers. We find that the role of habit varies widely and that estimation of a single habit effect suffers from aggregation bias. Thus, modeling farmer-level heterogeneity in the examination of habit and hedging is crucial.Bayesian econometrics, habit formation, hedging decisions, information sources, Agribusiness, Agricultural Finance, Farm Management, Financial Economics, Labor and Human Capital, Production Economics, Productivity Analysis, Research Methods/ Statistical Methods, C11, Q12, Q14,

GENERALIZED HEDGE RATIO ESTIMATION WITH AN UNKNOWN MODEL

Myers and Thompson (1989) pioneered the concept of a generalized approach to estimating hedge ratios, pointing out that the model specification could have a large impact on the hedge ratio estimated. While a huge empirical literature exists on estimating hedge ratios, the literature is lacking a formal treatment of model specification uncertainty. This research accomplishes that task by taking a Bayesian approach to hedge ratio estimation, where specification uncertainty is explicitly modeled. Specifically, we present a Bayesian approach to hedge ratio estimation that integrates over model specification uncertainty, yielding an optimal hedge ratio estimator that is robust to possible model specification because it is an average across a set of hedge ratios conditional on di erent models. Model specifications vary by exogenous variables (such as exports, stocks, and interest rates) and lag lengths included. The methodology is applied to data on corn and soybeans and results show the potential benefits and insights gained from such an approach.Marketing,

Generalized Hedge Ratio Estimation with an Unknown Model

Myers and Thompson (1989) noted that the model specification could have a large impact on the hedge ratio estimated. A huge literature exists on estimating hedge ratios, but the literature is lacking a formal treatment of model specification uncertainty. This research accomplishes that task by taking a Bayesian approach to hedge ratio estimation, where specification uncertainty is explicitly modeled. The methodology is applied to data on hedging of corn and soybeans and on cross-hedging of corn oil using soybean oil futures. Results show the potential benefits and insights gained from such an approach.Marketing,

PUTTING THE "ECON" INTO ECONOMETRICS

Should econometricians always incorporate economic theory in their models or only when unrestricted estimators are found to violate an inviolable theory? Using Monte Carlo experiments, we find that econometricians should use economic theory to the fullest extent possible. To paraphrase Leamer's classic article, we should put the "econ" into econometrics.Research Methods/ Statistical Methods,

ECONOMIC CRITERIA FOR EVALUATING COMMODITY PRICE FORECASTS

Forecasts of economic time series are often evaluated according to their accuracy as measured by either quantitative precision or qualitative reliability. We argue that consumers purchase forecasts for the potential utility gains from utilizing them, not for their accuracy. Using Monte Carlo techniques to incorporate the temporal heteroskedasticity inherent in asset returns, the expected utility of a set of qualitative forecasts is simulated for corn and soybean futures prices. Monetary values for forecasts of various reliability levels are derived. The method goes beyond statistical forecast evaluation, allowing individuals to incorporate their own utility function and trading system into valuing a set of asset price forecasts.Commodity prices, Forecast evaluation, Value of information, Consumer/Household Economics,

MULTIPLE COMPARISONS WITH THE BEST: BAYESIAN PRECISION MEASURES OF EFFICIENCY RANKINGS

A large literature exists on measuring the allocative and technical efficiency of a set of firms. A segment of this literature uses data envelopment analysis (DEA), creating relative efficiency rankings that are nonstochastic and thus cannot be evaluated according to the precision of the rankings. A parallel literature uses econometric techniques to estimate stochastic production frontiers or distance functions, providing at least the possibility of computing the precision of the resulting efficiency rankings. Recently, Horrace and Schmidt (2000) have applied sampling theoretic statistical techniques known as multiple comparisons with control (MCC) and multiple comparisons with the best (MCB) to the issue of measuring the precision of efficiency rankings. This paper offers a Bayesian multiple comparison alternative that we argue is simpler to implement, gives the researcher increased exibility over the type of comparison made, and provides greater, and more in-tuitive, information content. We demonstrate this method on technical efficiency rankings of a set of U.S. electric generating firms derived within a distance function framework.Research Methods/ Statistical Methods,

Looking for Cattle and Hog Cycles through a Bayesian Window

The agricultural economics literature, both academic and trade, has discussed the assumed presence of cycles in livestock markets such as cattle and hogs for a very long time. Since Jarvis (1974), there has been considerable discussion over how these cycles impact optimal economic decision making. Subsequent studies such as Rucker, Burt, and LaFrance (1984), Hayes and Schmitz (1987), Foster and Burt (1992), Rosen, Murphy, and Scheinkman (1994), and Hamilton and Kastens (2000) have all investigated some aspect of how biological factors, economic events, or economic actions could be causes of and/or responses to cycles in hog and cattle inventories. There has also been debate, again both in the academic and trade literature, over the length of the cycle(s) present in hog and cattle stocks. To provide both academics and producers with accurate information on the number and periods of cycles that might be present in hog and cattle inventories, this paper provides a purely statistical view of the matter. Using over 140 years of annual data on cattle and hog inventory levels, we estimate Bayesian autoregressive, trend-stationary models on cattle inventories, hog inventories, and the growth rate of cattle inventories. We then use those models to find the posterior distributions of both the number of cycles present in each series and the period lengths of those cycles. We find multiple cycles present in all three series. Cattle inventory results show clear evidence in favor of 4.5, 6, and 11 year cycles with other cycles present but not as clearly identified. Hog inventory results identify five cycles with periods of approximately 4.5, 5.4, 6.8, 10 and 13 years. The data on the growth rate in cattle stocks has similar cycles to the series on the stock levels.Bayesian econometrics, cattle cycles, hog cycles., Agribusiness, Livestock Production/Industries, Production Economics,

AN ECONOMIC EVALUATION OF COTTON AND PEANUT RESEARCH IN SOUTHEASTERN UNITED STATES

The purpose of this study was to utilize the economic surplus framework for evaluating the impact of investment in agricultural research. The economic impact measures used in this study were the total benefits and distribution of those benefits associated with investment in agricultural research. These results were used to calculate an internal rate of return on the investments. The focus of the research was on cotton and peanuts in the Southeast region of the United States. Two equations were estimated to determine the impacts of the money being spent on the research efforts of these two commodities. The results revealed positive benefits to consumers and producers exceeded the investment amount in each year for both commodities in the period. The total social benefits averaged about 201 million (1982) dollars annually for cotton research. Peanut research averaged about 191 million (1982) dollars resulting form research investment. The internal rates of return were 23.87 percent for cotton and 53.58 percent for peanuts, suggesting that past research investments produced a high return to society. This result does not conflict the results of other similar studies as those mentioned in the literature review.Research and Development/Tech Change/Emerging Technologies,