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This paper examines the role of mechanical refrigeration in temporal and spatial price relationships for regional egg markets in the United States, 1880–1911. Notably, this period encompasses an era in which widespread adoption of mechanical refrigeration greatly impacted the ability to briefly store otherwise perishable items. This development in turn altered observed price dynamics for many perishable commodities, including fresh eggs. We use a class of time series models, time–varying autoregressions (TVARs), to document both the structural change and the corresponding impact on spatial price dynamics for U.S. regional egg price relationships during the late 19th and early 20th centuries.

This paper explores the behavior of real commodity prices over a 50–year period. Attention is given to how the fundamentals for various commodity prices have changed with a special emphasis on behavior since the mid 2000s. To identify changing commodity price fundamentals we estimate shifting–mean autoregressions by using: the Bai and Perron (1998) procedure for estimating structural breaks; a SlowShift procedure that specifies intercepts to be nonlinear, potentially smooth functions of time; and low frequency Fourier functions. We find that the pattern in the timing of the various shifts is suggestive of the causal fundamentals underlying the recent boom.

Replaced with revised version of paper 06/28/11.

This chapter reviews the specification and application of the Deaton and Muellbauer (1980) Almost Ideal Demand System (AIDS) and the Christensen, Jorgensen, and Lau (1975) tranlog (TL) demand system. In so doing we examine various refinements to these models, including ways of incorporating demographic effects, methods by which curvature conditions can be imposed, and issues associated with incorporating structural change and seasonal effects. We also review methods for adjusting for autocorrelation in the model's residuals. A set of empirical examples for the AIDS and a the log TL version of the translog based on historical meat price and consumption data for the United States are also presented.

In this research we estimate the effect of El Nino Southern Oscillation (ENSO) over time on market dynamics for eight major vegetable oil prices. We estimate a system for vegetable oil prices by using a smooth transition vector error correction model (STVECM) to analyze impacts of ENSO events on production, and, more interestingly, their asymmetric nature. The results of estimated Exponential STVECM and Quadratic STAR models, respectively for the system of oil price equations and the ENSO variable regressions, suggest a smooth transition between ENSO regimes, and provide a better overall fit to the data than do linear models. Effects of the ENSO shock are analyzed using generalized impulse-response functions (GIRFs). The non-linear nature of these shocks is apparent, as the GIRFs are observed to be asymmetric depending on whether ENSO shocks are positive or negative. For most vegetable oil prices an ENSO shock has a permanent effect, meaning that prices advance to a new equilibrium level. Generally, a positive ENSO shock results in increased prices, and the opposite is true for the negative ENSO shock. The magnitude of the price change is largest for the coconut oil and palm kernel oil, and is the smallest for the ground nut oil. Also, it takes approximately two years for prices to stabilize at a new equilibrium level after the shock.

This appendix contains diagnostic test results and supplemental regression results from an evaluation of the Prebisch-Singer Hypothesis that applies smooth transition autoregressions (STARs) to relative prices for 24 primary commodities.

Market price dynamics for North American oriented strand board markets are examined. Specifically, the role of transactions costs are examined vis–`a–vis the law of one price. Weekly data for the January 3rd, 1995 through April 14th, 2006 period are used in the analysis. Nonlinearities induced by unobservable transactions costs are modeled by estimating smooth transition autoregressions (STARs). Results indicate that nonlinearity is an important feature of these markets and that the parity relationships implied by economic theory are generally supported by the STAR models. Implications for the efficiency of spatial market linkages are examined by estimating generalized impulse response functions.

This paper investigates non-linearity in spatial processes models and allows for a gradual regime-switching structure in the form of a smooth transition autoregressive process. Until now, applications of the smooth transition autoregressive (STAR) model have been largely confined to the time series context. The paper focuses on extending the non-linear smooth transition perspective to spatial processes models, in which spatial correlation is taken into account through the use of a so-called weights matrix identifying the topology of the spatial system. We start by deriving a non-linearity test for a simple spatial model, in which spatial correlation is only included in the transition function. Next, we propose a non-linearity test for a model that includes a spatially lagged dependent variable or spatially autocorrelated innovations as well. Monte Carlo simulations of the various test statistics are performed to examine their power and size. The proposed modeling framework is then used to identify convergence clubs in the context of U.S. county-level economic growth over the period 1963–2003.

This paper explores the use of time-varying regression models to model the effects of technical change in US Midwest Corn yields. The data extends from 1895 to 2005 encompassing the implementation of hybrid technologies and improvements in farm production practices.

In recent years the theoretical restrictions of consumer demand have been examined in post sample forecasting exercises. However, this work has uniformly ignored the concavity restrictions of consumer demand. In this paper we evaluate a series of Normalized Quadratic Inverse Demand System (NQIDS) specifications using rolling windows and generating one-- to four--step ahead forecasts. To estimate the models, eleven categories of South Atlantic fish are used from 1980 through 2001. In addition to the NQIDS, we also examine the forecasting performance of a purely time series model. We find that the best predictions are achieved using a composite forecast.

Replaced with revised version of paper 06/27/06.

The material contained herein is supplementary to the article named in the title and published in the American Journal of Agricultural Economics, Volume 88, Number 1, February 2006.

Market efficiency and unbiasedness tests are performed for the first time for three forest commodity futures markets: softwood lumber, oriented strand board (OSB), and northern bleached softwood kraft pulp (NBSK). The Johansen cointegration procedure is applied to test long-term market efficiency, while the standard error correction models (ECM) and ECM with GQARCH-in-mean process are also used to examine short-term market efficiency and unbiasedness. Results show that these markets are inefficient and biased in both the long-term and short-term. Results also indicate that no short-term time-varying risk premiums are found in these commodity futures markets.

This paper examines the effects of price uncertainty on agricultural productivity. Appelbaum(1991) provided an empirical framework to analyze the effects of uncertainty on firm behavior. We apply the model to the U.S. agricultural sector, using a parametric rather than a nonparametric approach to obtain the measurement of price uncertainty and risk. Keywords: risk, uncertainty, productivity

This paper compares the finite sample performance of subsample bootstrap and subsample jackknife techniques to the traditional bootstrap method when parameters are constrained to be on some boundary. To assess how these three methods perform in an empirical application, a negative semi-definite translog cost function is estimated using U.S. manufacturing data.

In many studies the assumption is made that traders only encounter one type of price risk. In reality, however, traders are exposed to multiple price risks, and often have several relevant derivative instruments available with which to hedge price uncertainty. In this study, commodity, foreign exchange, and freight futures contracts are analyzed for their effectiveness in reducing price uncertainty for international grain traders. A theoretical model is developed for a representative European importer to depict a realistic trading problem encountered by an international grain trading corporation exposed to more than one type of price risk. The traditional method of estimating hedge ratios by Ordinary Least Squares (OLS) is compared to the Seemingly Unrelated Regression (SUR) and the multivariate GARCH (MGARCH) methodology, which takes into account time-varying variances and covariances between the cash and futures markets. Explicit modeling of the time-variation in futures hedge ratios via the MGARCH methodology, using all derivatives and taking into account dependencies between markets results in a significant reduction in price risk for grain traders. The results also confirm that the unique, but underutilized, freight futures market is a potentially useful mechanism for reducing price uncertainty for international grain traders. The research undertaken in this study provides valuable information about reducing price uncertainty for international grain traders and gives a better understanding of the linkages between closely related markets.

This paper estimates an Almost Ideal Supply System using aggregate U.S. agricultural data. Share equations derived from an indirect production function yield elasticities that are consistent with production theory. A nested test comparing the Almost Ideal Supply System to the Translog Production Function finds little difference between the two models.

Assuming a competitive market, conditions are determined for when a steady-state equilibrium does not exist in the optimal dynamic management of a biological population. Irregular and unpredictable behavior (called “"chaos"”) can arise from fully rational economic decision making. High interest rate, adjustment costs, and an inelastic demand can contribute to market instability.

The role of price risk in sow farrowings is investigated by using bivariate ARCH-M and GARCH-M models and a nonparametric kernel estimator. To account for the relevant time horizon of irreversible supply decisions, predictions for mean price and conditional price variance are iterated forward. The empirical results vary markedly in terms of their implications for risk response in hog supply decisions, with the ARCH-M and GARCH-M models suggesting a small and negative risk effect. Estimates of the marginal risk premium also indicate moderate and variable departures from marginal cost pricing in sow farrowing supply decisions.

A model that includes bounded price variation and rational expectations by producers is estimated for the U.S. corn market. The resulting model specification is highly nonlinear though, since the probability of market equilibrium must be determined endogenously. Unlike previous research, the cross-equation restrictions implied by the rational expectations hypothesis are incorporated in the bounded prices model by using Fair and Taylor's (1983) procedure for obtaining maximum likelihood estimates of nonlinear rational expectations models. The resulting model is compared against a standard equilibrium model with naive expectations. The results show the bounded prices model is a superior specification. Copyright 1989 by MIT Press.

This article applies recent developments in time-series modeling to analyze the retail prices of beef, pork, and chicken. Specifically, generalized autoregressive conditional heteroscedasticity (GARCH) models were fitted to these data to determine if, unlike more traditional time-series models, the conditional variances of the underlying stochastic processes are nonconstant. The estimation results indicate that the constant conditional variances assumption can be rejected. Furthermore, ex post forecast intervals generated from the GARCH processes indicate that the forecasting accuracy of the estimated models has varied widely over time with substantial volatility occurring during the 1970s and early 1980s.

Romaine lettuce and leafy greens have been at the center of food safety concerns over the last several years. More specifically, romaine lettuce has been directly linked to seven(7) foodborne illness outbreaks and resulted in five(5) recalls over the eight(8) years period of January 1, 2012, to December 31, 2019. This paper estimates the effects that these food safety events have had on the price returns of romaine lettuce utilizing a series of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. Importantly, the GARCH models allowed us to capture the effects of the recall and illness outbreaks on both the returns and volatility of the romaine price series. We find that three (3) of the seven (7) illness outbreaks resulted in marked increases in the price returns - between 4.1% and 9.6%. Conversely, three (3) of the five (5) recalls reduced price returns - between 30% and 57%. However, the volatility is not found to be significantly nor to affect the price volatility significantly. We conclude that recalls serve as a market correction in the romaine lettuce market. Consequently, a continued focus on increasing traceability with in the romaine lettuce market will help to reduce price fluctuation and limit the number of illnesses resulting from outbreaks. Master of Science Romaine lettuce and leafy greens have been at the center of food safety concerns over the last several years. More specifically, romaine lettuce has been directly linked to 7 foodborne illness outbreaks and resulted in 5 recalls over the eight years, January 1, 2012, to December 31, 2019. This paper estimates the effects that these food safety events have had on the price returns of romaine lettuce. It was found that 3 of the seven illness outbreaks resulted in marked increases in the price returns - between 4.1% and 9.6% while, 3 of the 5 recalls reduced price returns - between 30% and 57%. However, the volatility is not found to be significantly nor to affect the price volatility significantly. It is concluded that recalls serve as a market correction in the romaine lettuce market. A continued focus on increasing traceability with in the romaine lettuce market will help to reduce price fluctuation and limit the number of illnesses resulting from outbreaks.

This dissertation is composed of two manuscripts in Production Economics and two manuscripts in International Agricultural Development. The first two manuscripts focus on production economics, and both are an exploration of nitrogen use and its impact on continuous corn production and profitability in Colorado. The first manuscript titled "Switching Regression Stochastic Plateau Production Functions––A Comparison of Alternative Specifications" proposes an alternative approach for estimating crop yield response functions using a frequentist approach. The second manuscript titled "Profitability Effects of Different Tillage Systems in Continuous Corn Rotations" explores the interaction between different tillage systems and nitrogen fertilization in irrigated continuous corn production in northeastern Colorado. We find that strip tillage is better suited for continuous corn production under the agro-climatic conditions in northeastern Colorado. The third and fourth manuscripts focus on international agricultural development and analyze the role of factors that influence the agricultural development of small-holder farmers in Ecuador, namely, markets, food value chains, risk preferences, and risk perceptions. The third manuscript titled "Does the Use of Specialty Varieties and Post-Harvest Practices Benefit Farmers? Cocoa Value Chains in Ecuador" analyzes the impact of the use of specialty cocoa varieties on small-scale farmers' income. It finds that the use of specialty cocoa varieties has a low impact on small-scale cocoa producers' income, and that post-harvest practices may lead to substantial price responses irrespective of the type of cocoa grown. Finally, the fourth manuscript titled "Linking Risk Preferences and Risk Perceptions of Climate Change Using Prospect Theory" explores how farmers' risk preferences correlate with their perceptions of climate risk. It finds that farmers that behave in accordance to the assumptions of prospect theory are more likely to perceive greater risks from climate change, that is, they are more likely to perceive the risks associated with climate change as being more threatening at a personal level. Since risk perception is a necessary prerequisite for adaptation, the results presented in this manuscript, have important policy implications for process of adoption of new technologies aimed at mitigating effects of climate change. Doctor of Philosophy This dissertation focuses on Production Economics and International Agricultural Development. The first two manuscripts focus on production economics, and both are an exploration of nitrogen use and its impact on continuous corn production and profitability in Colorado. The first manuscript proposes an alternative approach for estimating crop yield response functions to a single input. The second manuscript explores the interaction between different tillage practices and nitrogen fertilization in irrigated continuous corn production. The third and fourth manuscripts focus on international agricultural development, and they analyze the role of different factors that influence the agricultural development of small-holder farmers in Ecuador. The third manuscript analyzes cocoa markets in Ecuador. It finds that the use of gourmet cocoa varieties has a low impact on small-scale cocoa producers' income, and that post-harvest practices may lead to substantial benefits. Finally, the fourth manuscript explores farmers' risk behavior. It finds that farmers that behave in accordance to the assumptions of prospect theory, are more likely to show a greater risk perception of climate change, that is, they are more likely to perceive the risks associated with climate change as being more threatening at a personal level. Since risk perception is a necessary prerequisite for adaptation, the results presented in this manuscript, have important policy implications for the adoption process of new technologies aimed at mitigating effects of climate change.