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A dynamic system consisting of donations and various sports' success is estimated to examine relationships among winning and donations. Differences between Power 5, Non-power 5, and Football Championship Series alignments exist. Evidence that successful athletic programs have a positive impact on donations is found regardless of the conference alignment, but the impact varies by sport and alignment. The effects, however, are short lived. Donations, however, appear to have little impact on winning. There is some degree of interaction among winning in different sports.

The degree to which the law of one price holds (integration) along with determining each individual markets’ role in price discovery is examined for 11 major natural gas markets, six from the USA and five from Canada. Deregulation, technological advances, and trade agreements have opened the USA’s and Canada’s natural gas market to new and extensive interactions. The 11 natural gas market prices are tied together with ten long-run co-integration relationships with all markets included in the co-integration space, providing evidence the markets are integrated. The degree of integration varies by region. Markets geographically adjacent to each other tend to be more highly integrated than markets separated by distance. Further results indicate that there is no clear price leader among the 11 markets. Including more US and Canadian markets than previous studies, show markets in both eastern and western USA and Canada are important in the price discovery process.

Directed Acyclic Graphs (DAG's) and Error Correction Models (ECM's) are employed to analyze questions of price discovery between spatially separated commodity markets and the transportation market linking them together. Results from our analysis suggest these markets are highly interconnected but it is the inland commodity market that is strongly influenced by both the transportation and commodity export markets. However, the commodity markets affect the volatility of the transportation market over longer horizons. Our results suggest that transportation rates are critical in the price discovery process lending support for the recent development of exchange traded barge rate futures contracts.

The paper considers the conjecture that forecasts from preferred economic models or theories d-separate forecasts from less preferred models or theories from the Actual realization of the variable for which a scientific explanation is sought. D-separation provides a succinct notion to represent forecast dominance of one set of forecasts over another; it provides, as well, a criterion for model preference as a fundamental device for progress in economic science. We demonstrate these ideas with examples from three areas of economic modeling.

Econometritions frequently believe that standard instrumental variables (IV) methods can prove causal relationships. We review the relevant formal causal inference literature, and we demonstrate that this belief is not justified. Couching the problem in terms of falsification, we describe the more stringent conditions that are sufficient to reject a null hypothesis concerning observed, but not deliberately manipulated, variables of the form H 0 : A ↛ B in favor of an alternative hypothesis H A : A → B , even given the possibility of causally related unobserved variables. Rejection of such an H 0 can rely on the availability of two observed and appropriately related instruments. We also characterize, using Monte Carlo simulations, the confidence that can be placed on such judgments for linearly-related, jointly normal random variables. While the researcher will have limited control over the confidence level of such tests, type I errors occur with a probability of less than 0.15 (often substantially less) across a wide range of circumstances. The power of the test is limited if there are but few observations available and the strength of correspondence among the variables is weak. We demonstrate the method by testing a hypothesis with critically important policy implications relating to a possible cause of childhood malnourishment.

Evaluations of generic advertising programs by commodity check-off programs involve analyses of counterfactual scenarios in which advertising and promotion expenditures are set to zero over the program's history. In actual practice, the counterfactual is rarely realized. We present a case in which such a natural experiment occurred when generic advertising and promotion expenditures for U.S. orange juice were cut nearly to zero. Using structural econometric and autoregression models, we estimate losses in consumption and sales revenue and examine the time required for the market for orange juice to recover from the check-off's strategy of going nearly dark.

In Professor George Judge's pursuit of information recovery and isolating causality in noisy effects observational data, there is a critical distinction between deductive and inductive empirical analysis. For the former, we bring together a synthesis of the literature that has emerged since Koopmans' measurement with theory philosophy. For the latter, we present a host of methodologies that attempt to isolate the causal mechanisms existing in patterns revealed in noisy measurement data. The deductive focus is limited by available theoretical constructs, whereas the inductive focus is fraught with data mining complications, ultimately finding its potential validation in forecasting.

In Figure 1 A, I represent the causal flow from X to Y. However, both X and Y are influenced by (caused by ) a set of unobserved variables, L. In this simple model, ordinary least squares re- gression of Y on X will yield biased and in- consistent parameter estimates of OY/dX. [...]regression will yield systematically too large or too small of partial derivative estimates; sub- sequent elasticity estimates (the bread and butter of applied economic policy analysis) will be either too high or too low.

This article examines the price discovery performance of futures markets for storable and nonstorable commodities in the long run, allowing for the compounding factor of stochastic interest rates. The evidence shows that asset storability does not affect the existence of cointegration between cash and futures prices and the usefulness of future markets in predicting future cash prices. However, it may affect the magnitude of bias of futures markets’ estimates (or predictions) for future cash prices. These findings have several important implications for commodity production decision making, commodity hedging, and commodity price forecasting. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:279–300, 2001

We conduct Monte Carlo simulations to evaluate the use of information criteria (Akaike information criterion [AIC] and Schwarz information criterion [SC]) as an alternative to various probability-based tests for determining cointegrating rank in multivariate analysis. First, information criteria are used to determine cointegrating rank given the lag order in a levels vector autoregression. Second, information criteria are used to determine the lag order and cointegrating rank simultaneously. Results show that AIC has an advantage over trace tests for cointegrated or stationary processes in small samples. AIC does not perform well in large samples. The performance of SC is close to that of the trace test. SC shows better large sample results than AIC and the trace test, even if the series are close to nonstationary series or they contain large negative moving average components. We also find evidence that supports the joint estimation of lag order and cointegrating rank by the SC criterion. We conclude that information criteria can complement traditional parametric tests.We are grateful to Peter C.B. Phillips and an anonymous referee for their comments, which significantly improved the paper.