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Using resource dependency theory, the purpose of this paper is to examine what elements in the business environment may be associated with the formation and continuance of cartels.

The authors employ a unique data set of 148 cartel data points from the 1970s to 2008 which have at least one American company involved to quantitatively test causal relationships. The authors also interview key class action anti-trust attorneys for their views and opinions on the impact of these environmental factors on cartel formation and continuance.

The authors find statistically significant relationships between the pursuit and maintenance of industry profits and the dynamism in the industry, and illegal behavior as represented through price fixing by business cartels. The authors find that in the attorneys’ opinion, it is also the pursuit of individual corporate profits and munificence that are associated with these cartels.

This research furthers the understanding of organizational deviance which is critical given its impact on organizations, individuals, regulators, law enforcement, and the general public.

This research is a first step in considering cartel activity in a way that encompasses external influences in a new and innovative manner and as a tool to help researchers and practitioners better understand how organizational deviance, as manifested through illegal corporate activity, can be anticipated, identified, and prevented.

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

Small-scale VARs are widely used in macroeconomics for forecasting US output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real-time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters, and the Federal Reserve Board's Greenbook as benchmarks.