Research Papers

This paper compares the performance of different policy rules, focusing on simple feedback rules versus rules that are optimal, given knowledge of the correct economic structure and the appropriate loss function for the policymaker. First, we compare rule performance when the correct model is not known. Second, we compare rule performance with respect to the frequency-specific behavior for variables of interest.

We are interested in assessing the case that can be made for optimal rules as opposed to simple rules for policy, focusing on monetary policy rules.

The case for optimal policy rules is weakened when one considers the performance of the rules in the presence of model uncertainty and with respect to frequency-specific effects.

We explore ways to integrate model uncertainty into policy evaluation. We describe a general framework that includes both model averaging methods and some measures that describe whether policies and their consequences are model dependent. These general ideas are then applied to assess simple monetary policy rules for some standard New Keynesian specifications.

While model averaging is a powerful tool in addressing model uncertainty, a policymaker may want more information than simply a summary statistic of the effects of a policy on outcomes where model dependence has been integrated. An innovation in this paper is moving beyond model averaging methods in analyzing policies when model uncertainty is present.

Within macroeconomics, increasing attention is being given to the positive and normative implications of model uncertainty.

We developed a general framework for conducting local robustness analysis, focusing on calculating control solutions that are optimal against the least favorable model close to an initial baseline. We characterized Nash and Stackelberg equilibria for control choices and applied our formulation to discrete time control problems using frequency domain methods.

We aimed to address the need for robust control solutions in environments with model uncertainty. By developing a framework for local robustness analysis, we sought to provide optimal control strategies that remain effective under varying conditions close to a baseline model.

Our results highlight the fundamental limits to the efficacy of controls in uncertain environments. Understanding these limits and characterizing robust control solutions have significant implications for policy-making, particularly in the context of monetary policy, where robust and effective controls are crucial for economic stability.

We show how to develop an analysis of social interactions that builds the interdependencies that characterize complex systems into a framework consistent with individual decision making.

A major weakness of the social interactions literature as it is currently constituted is the absence of strong connections between theory and empirics. By this, we mean that there has been little effort to employ theoretical models of social interactions in structural estimation. This paper extends our previous work on binary choice models to account for multinomial choice.

Within economics, there has developed an increasing recognition that social interactions may play a major role in explaining a range of individual behaviors.

This article provides an overview of the empirical side of the growing economic complexity. It explores the state of interplay between recent efforts to introduce complex systems methods into economics and the understanding of empirical phenomena.

The evidence in the existing literature is far from decisive and is amenable to alternative interpretations. It is therefore unclear whether this work has provided evidence in support of economic complexity per se.

There is an increasing application in the economics of ideas from complex systems theory.

Our paper is designed to consider some of the general issues that need to be addressed in evaluating racial profiling as a public policy question. Placing the discussion in the context of efficiency/equity trade-offs, this paper discusses profiling in the context of police traffic stops where the intent is to identify drug carriers.

While there is a growing body of theoretical and empirical work on profiling, relatively little attention has been paid to integrating efficiency and equity considerations in an overall assessment.

Racial profiling continues to be a contentious issue in American society. While official policing policies have in many places banned profiling, the impact of such regulations is unknown.

This paper provides a survey and synthesis of econometric tools that have been employed to study economic growth. It will both survey the body of econometric and statistical methods that have been brought to bear on growth questions and provide some assessments of the value of these tools.

The difficulties inherent in the empirical study of growth have prompted the field to evolve continuously, and to adopt a wide range of methods. We argue that a sufficiently rich set of statistical tools for the study of growth have been developed and applied that they collectively define an area of growth econometrics.

The empirical study of economic growth occupies a position that is notably uneasy. Understanding the wealth of nations is one of the oldest and most important research agendas in the entire discipline. At the same time, it is also one of the areas in which genuine progress seems hardest to achieve.

We survey the modern economics literature on the role of neighborhoods in influencing socioeconomic outcomes, covering a range of theoretical, econometric, and empirical topics.

Studies argue that geographic neighborhood influences are crucial for understanding inner city poverty. Methodological advances in economic theory now incorporate spillover effects into microeconomic models, similar to those in growth contexts. Additionally, analyzing neighborhood effects in game theory has become a significant research area.

Neighborhood effects have been analyzed in a range of theoretical and applied contexts and have proven to be of interest in understanding questions ranging from the asymptotic properties of various evolutionary games to explaining the persistence of poverty in inner cities.

We described approaches to macroeconomic policy evaluation under uncertainty about the economic environment’s structure. Our methods are designed to address several forms of uncertainty, such as ambiguity about the appropriate economic theory or functional forms required for statistical analysis. We incorporated model uncertainty into formal statistical inference methods to facilitate robust policy evaluation.

We aimed to provide a robust framework for policy evaluation in macroeconomic contexts where fundamental disagreements exist regarding determinants of the problem under study. Recognizing the limitations of traditional calibration methods, we sought to develop an approach that incorporates model uncertainty into policy evaluation.

Incorporating model uncertainty into policy evaluation enhances the robustness and reliability of macroeconomic policies. Our approach allows for more informed decision-making in the presence of theoretical and structural uncertainties, ultimately contributing to more effective and adaptive economic policies.

We developed general conditions under which complementarities between individual agents imply that assortative matching is efficient. Our analysis covered equal-sized groups, varying group sizes, sequences of replications, and the effects of feedback from group composition on efficient allocations.

We aimed to understand the efficiency of assortative matching in the presence of agent complementarities. By examining different scenarios and conditions, we sought to identify when and why assortative matching leads to efficient outcomes.

Our findings highlight the complexities in achieving efficient assortative matching in an economy. Understanding these complexities and the conditions for efficiency is crucial for designing policies and mechanisms that promote optimal allocation of resources and improve economic outcomes.