William A. Barnett

Economic Bifurcation and Chaos provides a unified presentation of the contributions to the literature on economic bifurcation, nonlinear dynamics, and chaos by William A Barnett and his coauthors. The span of the research begins in 1988 with Barnett's initial finding of chaos in economic data. The book continues with subsequent findings of bifurcation in all dynamical macroeconomic models so far tested by Barnett and his coauthors. The research covered extends over 3 decades to his recent findings of Shilnikov chaos in New Keynesian models of the UK and US economies. While methodology for testing for bifurcation, nonlinear dynamics, and chaos are fundamental to the research, the book's ultimate objectives focus on implications for robustness of dynamical inferences and implications for policy.Bifurcation subsets are found stratifying the parameter space of all tested macroeconomic models, with confidence regions of parameter estimates crossing the boundaries between those subsets. A robustness problem results from policy simulations conducted only at point estimates of model parameters. Such simulations should be conducted at various locations throughout the confidence region to explore the dynamical implications of such models.A more dramatic result is acquired in the special case of New Keynesian models during periods of active Taylor rule setting of short-term interest rates. That policy, without the imposition of a long run transversality condition, bifurcates the economy's dynamics to Shilnikov chaos, implying unintended long run downward drift of interest rates within the Shilnikov fractal attractor set. To control the drift, there needs to be a second policy instrument focused on the long run.

Bifurcation of Macroeconometric Models and Robustness of Dynamical Inferences provides an overview of the classes of macroeconometric models for which bifurcation experiments have so far been run, and emphasizes the implications for lack of robustness of conventional dynamical inferences from macroeconometric policy simulations. By making this detailed survey of past bifurcation experiments available, the authors hepe to encourage and facilitate further research on this problem with other models, and to emphasize the need for simulations at various points within the confidence regions of macroeconometric models rather than at only point estimates.

A leading economist contends that the recent financial crisis was caused not by the failure of mainstream economics but by corrupted monetary data constructed without reference to economics.Blame for the recent financial crisis and subsequent recession has commonly been assigned to everyone from Wall Street firms to individual homeowners. It has been widely argued that the crisis and recession were caused by "greed" and the failure of mainstream economics. In Getting It Wrong, leading economist William Barnett argues instead that there was too little use of the relevant economics, especially from the literature on economic measurement. Barnett contends that as financial instruments became more complex, the simple-sum monetary aggregation formulas used by central banks, including the U.S. Federal Reserve, became obsolete. Instead, a major increase in public availability of best-practice data was needed. Households, firms, and governments, lacking the requisite information, incorrectly assessed systemic risk and significantly increased their leverage and risk-taking activities. Better financial data, Barnett argues, could have signaled the misperceptions and prevented the erroneous systemic-risk assessments.When extensive, best-practice information is not available from the central bank, increased regulation can constrain the adverse consequences of ill-informed decisions. Instead, there was deregulation. The result, Barnett argues, was a worst-case toxic mix: increasing complexity of financial instruments, inadequate and poor-quality data, and declining regulation.Following his accessible narrative of the deep causes of the crisis and the long history of private and public errors, Barnett provides technical appendixes, containing the mathematical analysis supporting his arguments.

The book surveys modern literature on financial aggregation and index number theory, with special emphasis on the contributions of the book's two coauthors. In addition to an introduction and a systematic survey chapter unifying the rest of the book, this publication contains reprints of six published articles central to the survey chapter. Financial Aggregation and Index Number Theory provides a reference work for financial data researchers and users of central bank data, placing emphasis on possible improvements in such data from use of the microeconomic index number and aggregation theory.

Nonlinear Dynamics and Economics presents some of the recent developments in nonlinear economic dynamics along with related research from associated fields, including mathematics, statistics, biology, and physics. Specific areas covered include instability in economic theory, nonlinearity in financial markets, tests for nonlinearity and chaos, frequency domain methods, nonlinear business cycles, and nonlinear prediction and forecasting. This volume comprises the tenth in the International Symposia in Economic Theory and Econometrics series under the general editorship of William Barnett. This proceedings volume includes revisions of the most important papers presented at a conference held at the European University Institute in Florence on July 6-17, 1992, along with revisions of the related, invited papers presented at the annual meetings of the American Statistical Association held in San Francisco on August 8-12, 1993.