In this article, we measure and interpret the common “factors” that describe money market returns. Results are presented for both three‐and four‐factor models. We find that the three‐factor model explains, on average, 86 percent of the total variation in most money market returns while the four‐factor model explains, on average, 90 percent of this variation. Using mimicking portfolios, we provide an interpretation of the systematic risks represented by these factors.

From page 417-- "Economists have long been interested in explaining the observed instability of economic aggregates. Though several reasons for variation in the pace of production are easily given, such as exogenous variation in tastes or in production possibilities, it it hard to see why there should be large variations in those factors that are synchronized across the entire economy. Instead , it seems more likely to suppose that variations in demand or in production costs in different parts of the economy should be largely independent and hence that a law of large numbers would imply that significant variations in aggregate activity (relative to the typical size of aggregate activity) are not likely to occur. ... Here we explore another type of explanation, which relies on an entirely different mechanism. Our proposal is that the effects of many small independent shocks to different sectors of the economy need not cancel out in the aggregate, due to the presence of significantly nonlinear, strongly localized interactions between different parts of the economy. The type of macroscopic instability that can result has been studied by condensed-matter physicists, under the name of 'self-organized criticality' (Per Bak and Kan Chen, 1991)."

In this article, the authors measure and interpret the common 'factors' that describe money market returns. Results are presented for both three- and four-factor models. The authors find that the three-factor model explains, on average, 86 percent of the total variation in most money market returns while the four-factor model explains, on average, 90 percent of this variation. Using mimicking portfolios, they provide an interpretation of the systematic risks represented by these factors.

U.S. beef cattle stocks are among the most periodic economic time series. A theory of cattle cycles is constructed on the basis of breeding stock inventory decisions. The low fertility rate of cows and substantial lags and future feedback between fertility and consumption decisions cause the demographic structure of the herd to respond cyclically to exogenous shocks in demand and production costs. Known demographic parameters of cattle imply sharp numerical benchmarks for the resulting dynamic system and closely compare with independent econometric time-series estimates over the 1875-1990 period. The model fits extremely well.

U.S. beef cattle stocks are among the most periodic economic time series. A theory of cattle cycles is constructed on the basis of breeding stock inventory decisions. The low fertility rate of cows and substantial lags and future feedback between fertility and consumption decisions cause the demographic structure of the herd to respond cyclically to exogenous shocks in demand and production costs. Known demographic parameters of cattle imply sharp numerical benchmarks for the resulting dynamic system and closely compare with independent econometric time-series estimates over the 1875-1990 period. The model fits extremely well.