Elements of a Theory of Design Limits to Optimal Policy

This paper presents a framework for understanding the limits that exist in optimal policy design in dynamic contexts. We consider the design of policies in the context of dynamic linear models. Fundamental design limits exist for policy rules in such environments in the sense that any policy rule embodies tradeoffs between the magnitudes of different frequency-specific components of the variance. Hence policies that are effective in eliminating low frequency variance components of a state variable can only do so at the cost of exacerbating high frequency variance components, and vice versa. Examples of the implications of such tradeoffs are considered.

Discrete Choice with Social Interactions I: Theory

This paper provides an analysis of aggregate behavioral outcomes when individual utility exhibits social interaction effects. We study generalized logistic models of individual choice which incorporate terms reflecting the desire of individuals to conform to the behavior of others in an environment of noncooperative decisionmaking. Laws of large numbers are generated in such environments. Multiplicity of equilibria in these models, which are equivalent to the existence of multiple self-consistent means for average choice behavior, will exist when the social interactions exceed a particular threshold. Local stability of these multiple equilibria is also studied. The properties of the noncooperative economy are contrasted with the properties of an economy in which a social planner determines the set of individual choices. The model is additionally shown to be well suited to explaining a number of empirical phenomena, such as threshold effects in individual behavior, ethnic group fixed effects of income equations, and large cross-group differences in binary choice behavior.

Multinomial Choice with Social Interactions

This paper develops a model of individual decisionmaking in the presence of social interactions when the number of available choices is finite. We show how a multinomial logit model framework may be used to model such decisions in a way that permits a tight integration of theory and econometrics. Conditions are given under which aggregate choice behavior in a population exhibits multiple self-consistent equilibria. An econometric version of the model is shown to be identified under relatively weka conditions. That analysis is extended to allow for general error distributions and some preliminary ways to account for the endogeneity of group memberships are developed.

Model Uncertainty and Policy Evaluation: Some Theory and Empirics

This paper explores ways to integrate model uncertainty into policy evaluation. We first describe a general framework for the incorporation of model uncertainty into standard econometric calculations. This framework employs Bayesian model averaging methods that have begun to appear in a range of economic studies. Second, we illustrate these general ideas in the context of assessment of simple monetary policy rules for some standard New Keynesian specifications. The specifications vary in their treatment of expectations as well as in the dynamics of output and inflation. We conclude that the Taylor rule has good robustness properties, but may reasonably be challenged in overall quality with respect to stabilization by alternative simple rules that also condition on lagged interest rates, even though these rules employ parameters that are set without accounting for model uncertainty.

Policy Evaluation in Uncertain Economic Environments

This paper develops a decision-theoretic approach to policy analysis. We argue that policy evaluation should be conducted on the basis of two factors: the policymaker's preferences, and the conditional distribution of the outcomes of interest given a policy and available information. From this perspective, the common practice of conditioning on a particular model is often inappropriate, since model uncertainty is an important element of policy evaluation. We advocate the use of model averaging to account for model uncertainty and show how it may be applied to policy evaluation exercises. We illustrate our approach with applications to monetary policy and to growth policy.

Time Dependent Clustering Analysis of the Second BATSE Gamma-Ray Burst Catalog

A time dependent two-point correlation-function analysis of the BATSE 2B catalog finds no evidence of burst repetition. As part of this analysis, we discuss the effects of sky exposure on the observability of burst repetition and present the equation describing the signature of burst repetition in the data. For a model of all burst repetition from a source occurring in less than five days we derive upper limits on the number of bursts in the catalog from repeaters and model-dependent upper limits on the fraction of burst sources that produce multiple outbursts.Comment: To appear in the Astrophysical Journal Letters, uuencoded compressed PostScript, 11 pages with 4 embedded figure

Policy Evaluation in Uncertain Economic Environments

This paper develops a general framework for economic policy evaluation. Using ideas from statistical decision theory, it argues that conventional approaches fail to appropriately integrate econometric analysis into evaluation problems. Further, it is argued that evaluation of alternative policies should explicitly account for uncertainty about the appropriate model of the economy. The paper shows how to develop an explicitly decision-theoretic approach to policy evaluation and how to incorporate model uncertainty into such an analysis. The theoretical implications of model uncertainty are explored in a set of examples, with a specific focus on how to design policies that are robust against such uncertainty. Finally, the framework is applied to the evaluation of monetary policy rules and to the analysis of tariff reductions as a way to increase aggregate economic growth.macroeconomics, Policy Evaluation, Uncertain Economic Environments