Aggregate behavior

Abstract: This paper proposes that idiosyncratic firm-level fluctuations can explain an important part of aggregate shocks, and provide a microfoundation for aggregate productivity shocks. Existing research has focused on using aggregate shocks to explain business cycles, arguing that individual firm shocks average out in aggregate. I show that this argument breaks down if the distribution of firm sizes is fat-tailed, as documented empirically. The idiosyncratic movements of the largest 100 firms in the US appear to explain about one third of variations in output and the Solow residual. This "granular" hypothesis suggests new directions for macroeconomic research, in particular that macroeconomic questions can be clarified by looking at the behavior of large firms. This paper's ideas and analytical results may also be useful to think about the fluctuations of other economic aggregates, such as exports or the trade balance.

Abstract: This paper examines the empirical relationship in the postwar United States between the aggregate business cycle and various aspects of the macroeconomy, such as production, interest rates, prices, productivity, sectoral employment, investment, income, and consumption. This is done by examining the strength of the relationship between the aggregate cycle and the cyclical components of individual time series, whether individual series lead or lag the cycle, and whether individual series are useful in predicting aggregate fluctuations. The paper also reviews some additional empirical regularities in the U.S. economy, including the Phillips curve and some long-run relationships, in particular long-run money demand, long-run properties of interest rates and the yield curve, and the long-run properties of the shares in output of consumption, investment and government spending.

Abstract: This paper tests implications of full consumption insurance. The object is to determine how much mileage can be obtained from a model with complete markets, with such features as private information or liquidity constraints omitted. The implication exploited is that individual consumption responds to aggregate risk but not to idiosyncratic risk. The test involves regressing the change in household consumption onto the change in aggregate consumption and other right-hand-side variables such as the change in household income and change in employment status. All variables other than the change in aggregate consumption are predicted to be insignificant in explaining the change in household consumption. With observations on consumption and income for 10,695 households from the Consumer Expenditure Survey, the results are mixed. The results for one specification (exponential utility) are mostly consistent with full consumption insurance; the results for the other specification (power utility) are not.

Abstract: Economic analysis has largely avoided questions about the way in which economic agents make choices when confronted by a perpetually novel and evolving world. As a result, there are outstanding questions of great interest to economics in areas ranging from technological innovation to strategic learning in games. This is so, despite the importance of the questions, because standard tools and formal models are ill-tuned for answering such questions. However, recent advances in computer-based modeling techniques, and in the subdiscipline of artificial intelligence called machine learning, offer new possibilities. Artificial adaptive agents (AAA) can be defined and can be tested in a wide variety of artificial worlds that evolve over extended periods of time. The resulting complex adaptive systems can be examined both computationally and analytically, offering new ways of experimenting with and theorizing about adaptive economic agents. Many economic systems can be classified as complex adaptive systems. Such a system is complex in a special sense: (i) It consists of a network of interacting agents (processes, elements); (ii) it exhibits a dynamic, aggregate behavior that emerges from the individual activities of the agents; and (iii) its aggregate behavior can be described without a detailed knowledge of the behavior of the individual agents. An agent in such a system is adaptive if it satisfies an additional pair of criteria: the actions of the agent in its environment can be assigned a value (performance, utility, payoff, fitness, or the like); and the agent behaves so as to increase this value over time. A complex adaptive system, then, is a complex system containing adaptive agents, networked so that the environment of each adaptive agent includes other agents in the system. Complex adaptive systems usually operate far from a global optimum or attractor. Such systems exhibit many levels of aggregation, organization, and interaction, each level having its own time scale and characteristic behavior. Any given level can usually be described in terms of local niches that can be exploited by particular adaptations. The niches are various, so it is rare that any given agent can exploit all of them, as rare as finding a universal competitor in a tropical forest. Moreover, niches are continually created by new adaptations. It is because of this ongoing evolution of the niches, and the perpetual novelty that results, that the system operates far from any global attractor. Improvements are always possible and, indeed, occur regularly. The everexpanding range of technologies and products in an economy, or the everimproving strategies in a game like chess, provide familiar examples. Adaptive systems may settle down temporarily at a local optimum, where performance is good in a comparative sense, but they are usually uninteresting if they remain at that optimum for an extended period. A theory of complex adaptive systems based on AAA makes possible the development of well-defined, yet flexible, models that exhibit emergent behavior. Such models can capture a wide range of economic phenomena precisely, even though the development of a general mathematical theory of complex adaptive systems is still in its early stages.' The AAA models complement current theoretical directions; they are