TESTING MARKET EQUILIBRIUM: IS COINTEGRATION INFORMATIVE?

Cointegration methods are increasingly used to test for market efficiency and integration. The economic rationale for these tests, however, is generally unclear. Using a simple spatial equilibrium model to simulate equilibrium price behavior, it is shown that prices in a well-integrated, efficient market need not be cointegrated. Furthermore, the number of cointegrating relationships among prices is not a good indicator of the degree to which a market is integrated.Demand and Price Analysis,

EXPERIMENTAL MARKETS USING THE ELECTRONIC MARKET PLACE (EMP)

A computer system for implementing electronic markets on networks of personal computers is described. The program allows a researcher or teacher to design market simulations to meet a variety of goals, and records a complete set of market activities for analysis. Illustrations of example markets are provided, and the classroom application of market simulations in teaching agricultural economics is discussed.Computer software, Experimental economics, Simulations, Marketing,

PROBABILISTIC PRICE FORECASTS BASED ON COMMODITY OPTION VALUES

Demand and Price Analysis,

A TERM STRUCTURE MODEL FOR AGRICULTURAL FUTURES

An extension of Schwartz's model of futures price term structure that includes seasonality is developed. The approach allows futures prices for all maturities to be estimated simultaneously by exploiting arbitrage relationships. An application to wheat futures prices is presented.futures markets, price analysis, Demand and Price Analysis, Marketing,

SEQUENTIAL REGRESSION: A FLEXIBLE TOOL FOR TIME SERIES MODELING

Research Methods/ Statistical Methods,

Learning about a Moving Target in Resource Management: Optimal Bayesian Disease Control

Resource managers must often make difficult choices in the face of imperfectly observed and dynamically changing systems (e.g., livestock, fisheries, water, and invasive species). A rich set of techniques exists for identifying optimal choices when that uncertainty is assumed to be understood and irreducible. Standard optimization approaches, however, cannot address situations in which reducible uncertainty applies to either system behavior or environmental states. The adaptive management literature overcomes this limitation with tools for optimal learning, but has been limited to highly simplified models with state and action spaces that are discrete and small. We overcome this problem by using a recently developed extension of the Partially Observable Markov Decision Process (POMDP) framework to allow for learning about a continuous state. We illustrate this methodology by exploring optimal control of bovine tuberculosis in New Zealand cattle. Disease testing—the control variable—serves to identify herds for treatment and provides information on prevalence, which is both imperfectly observed and subject to change due to controllable and uncontrollable factors. We find substantial efficiency losses from both ignoring learning (standard stochastic optimization) and from simplifying system dynamics (to facilitate a typical, simple learning model), though the latter effect dominates in our setting. We also find that under an adaptive management approach, simplifying dynamics can lead to a belief trap in which information gathering ceases, beliefs become increasingly inaccurate, and losses abound

MODELING SPATIAL DEPENDENCE AND SPATIAL HETEROGENEITY IN COUNTY YIELD FORECASTING MODELS

The implications of ignoring potential spatial dependence in county-level yield data are discussed. Spatial dependence in a county-level yield data set is identified and methods for correcting the dependence via spatial weighting matrices and generalized least squares regression are performed. The paper also examines how the spatial dependence declines as the distance between observations increases.Productivity Analysis, Research Methods/ Statistical Methods,

BT COTTON REFUGE POLICY

Since cotton producers do not own legal rights to kill insect populations that are susceptible to insecticides, individual producers may have no incentive to account for future, insecticide-resistance productivity losses arising from their pest-management decisions. As a result, the collective actions of producers may increase the rate of resistance development relative to the rate that maximizes social welfare. Concerns regarding insect-pest development of resistance to Bt cotton prompted the Environmental Protection Agency to establish legal limits on the proportion of total acres individual producers may plant, representing the first attempt to regulate the development of insecticide resistance and the first instance of the use of refuge as a policy instrument. Ever since Carlson and Castle first pointed out the resource characteristics of insecticide susceptibility, pest management in the presence of increasing resistance has been viewed as an exhaustible resource allocation problem, and many studies have examined efficient insecticide use in this setting. Resistance management studies found in the economics literature, however, have examined single-insect single-insecticide problems almost exclusively. The majority of genetic and entomological studies have followed suit. Since cotton producers routinely use multiple insecticides and insecticide mixtures to manage multiple insect pests, and since simulation and empirical evidence suggests that toxin mixtures can affect the rate of resistance development to component toxins, the standard model may not be well suited for the examination of refuge policies under cotton production settings. Static refuge policies that maximize the present value of profit flows attainable by producers over five- and 10-year planning horizons are examined using a deterministic, operational model that accounts for short- and long-run features of production and resistance development. The model accounts for the development of resistance in two cotton insect pests to Bt cotton and a popular conventional insecticide, and relationships between refuge policy, insecticide resistance, producer profit and producer behavior in Louisiana. The model is used to examine relationships between resistance simulation model parameters and refuge policies and comparative advantages between treated and untreated refuge policies.Agricultural and Food Policy,

Modeling precision treatment of breast cancer

Background: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified

Galaxy Clusters Associated with Short GRBs. II. Predictions for the Rate of Short GRBs in Field and Cluster Early-Type Galaxies

We determine the relative rates of short GRBs in cluster and field early-type galaxies as a function of the age probability distribution of their progenitors, P(\tau) \propto \tau^n. This analysis takes advantage of the difference in the growth of stellar mass in clusters and in the field, which arises from the combined effects of the galaxy stellar mass function, the early-type fraction, and the dependence of star formation history on mass and environment. This approach complements the use of the early- to late-type host galaxy ratio, with the added benefit that the star formation histories of early-type galaxies are simpler than those of late-type galaxies, and any systematic differences between progenitors in early- and late-type galaxies are removed. We find that the ratio varies from R(cluster)/R(field) ~ 0.5 for n = -2 to ~ 3 for n = 2. Current observations indicate a ratio of about 2, corresponding to n ~ 0 - 1. This is similar to the value inferred from the ratio of short GRBs in early- and late-type hosts, but it differs from the value of n ~ -1 for NS binaries in the Milky Way. We stress that this general approach can be easily modified with improved knowledge of the effects of environment and mass on the build-up of stellar mass, as well as the effect of globular clusters on the short GRB rate. It can also be used to assess the age distribution of Type Ia supernova progenitors.Comment: ApJ accepted versio