Chaos detection in economics. Metric versus topological tools

In their paper Frank F., Gencay R., and Stengos T., (1988) analyze the quarterly macroeconomic data from 1960 to 1988 for West Germany, Italy, Japan and England. The goal was to check for the presence of deterministic chaos. To ensure that the data analysed was stationary they used a first difference then tried a linear fit. Using a reasonable AR specification for each time series their conclusion was that time series showed different structures. In particular the non linear structure was present in the time series of Japan. Nevertheless the application of metric tools for detecting chaos (correlation dimension and Lyapunov exponent) didn’t show presence of chaos in any time series. Starting from this conclusion we applied a topological tool Visual Recurrence Analysis to these time series to compare the results. The purpose is to verify if the analysis performed by a topological tool could give results different from ones obtained using a metric tool.economics time series, chaos, and topological tool

Chaotic Time Series Analysis in Economics: Balance and Perspectives

To show that a mathematical model exhibits chaotic behaviour does not prove that chaos is also present in the corresponding data. To convincingly show that a system behaves chaotically, chaos has to be identified directly from the data. From an empirical point of view, it is difficult to distinguish between fluctuations provoked by random shocks and endogenous fluctuations determined by the nonlinear nature of the relation between economic aggregates. For this purpose, chaos tests test are developed to investigate the basic features of chaotic phenomena: nonlinearity, fractal attractor, and sensitivity to initial conditions. The aim of the paper is not to review the large body of work concerning nonlinear time series analysis in economics, about which much has been written, but rather to focus on the new techniques developed to detect chaotic behaviours in the data. More specifically, our attention will be devoted to reviewing the results reached by the application of these techniques to economic and financial time series and to understand why chaos theory, after a period of growing interest, appears now not to be such an interesting and promising research area.Economic dynamics, nonlinearity, tests for chaos, chaos

The chaotic system and new perspectives for economics methodology. A note

Historically, economists have, whenever possible, used linear equations to model economic phenomena, because they are easy to manipulate and usually yield unique solutions. However, it now has become impossible to ignore the fact that many important and interesting phenomena are not amenable to such treatment. With the chaos theory it is possible to take into account those aspects of the phenomena. The theory of chaos is challenging many of the fundamental presuppositions of the traditional older Newtonian world view of science. The implications of the new science vision will be explored starting from physics to arrive at economics in terms of their challenges to the traditional methodological views. In particular the implications of chaos control theory for the economics will be highlighted. The purpose of this paper is to show why the economists can no longer ignore that economics is a complex system and how the application of chaos control methods could improve the system's economic performance

Chaos detection in economics. Metric versus topological tools

In their paper Frank F., Gencay R., and Stengos T., (1988) analyze the quarterly macroeconomic data from 1960 to 1988 for West Germany, Italy, Japan and England. The goal was to check for the presence of deterministic chaos. To ensure that the data analysed was stationary they used a first difference then tried a linear fit. Using a reasonable AR specification for each time series their conclusion was that time series showed different structures. In particular the non linear structure was present in the time series of Japan. Nevertheless the application of metric tools for detecting chaos (correlation dimension and Lyapunov exponent) didn’t show presence of chaos in any time series. Starting from this conclusion we applied a topological tool Visual Recurrence Analysis to these time series to compare the results. The purpose is to verify if the analysis performed by a topological tool could give results different from ones obtained using a metric tool

Chaos detection in economics. Metric versus topological tools

In their paper Frank F., Gencay R., and Stengos T., (1988) analyze the quarterly macroeconomic data from 1960 to 1988 for West Germany, Italy, Japan and England. The goal was to check for the presence of deterministic chaos. To ensure that the data analysed was stationary they used a first difference then tried a linear fit. Using a reasonable AR specification for each time series their conclusion was that time series showed different structures. In particular the non linear structure was present in the time series of Japan. Nevertheless the application of metric tools for detecting chaos (correlation dimension and Lyapunov exponent) didn’t show presence of chaos in any time series. Starting from this conclusion we applied a topological tool Visual Recurrence Analysis to these time series to compare the results. The purpose is to verify if the analysis performed by a topological tool could give results different from ones obtained using a metric tool

sharing economy for an economic taxonomy

Sharing economy is an emerging phenomenon without a consolidated economic approach. We focus on main economic characteristics of sharing economy, as a category of peer-to-peer markets, which include collaborative consumption and redistribution markets. Peer-to-peer markets create a positive externality when exploiting idling capacity, adding further exchange advantages to those usually related to classical two-sided markets. Sharing economy differs from peer-to-peer redistribution markets, where a transfer of full ownership of goods occurs, because goods are used to provide services excludable but non-rivalrous. Problems arising from sharing economy growth are briefly analysed

Fitness landscape and tax planning: NK model for fiscal federalism

Economic models of Fiscal Federalism, according to different settings, are generally linear and static, offering unique and deterministic solutions starting with simplifying assumptions. This paper rises from the idea to investigate how the decision-makers, abandoning their traditional economic models and focusing, instead, the attention on innovative components of evolutionary economics, can achieve better performance results, to organize and to optimize an economic system based on Fiscal Federalism. For this purpose, Fiscal Federalism must be understood as a dense network of economic relationships between different complex adaptive and co-evolving systems, the jurisdictions, linked by strong interdependencies. A better understanding of the links between interdependence will be provided by the Kauffman’ NK-model. The relevance of the NK-model in the study of economic organizations has been detected several times in the literature. These studies, however, neglect the problem of co-evolution, which instead underpins this paper

Motivation, Incentives and Performance: An Interdisciplinary Review

This review aims to extend the application of economic knowledge to evidence supplied by other research areas on the relationships between incentives, motivation and performance. Six areas of investigation have been selected based on their potential contribution in addressing three issues relevant to economics. The first issue concerns the distinction between intrinsic and prosocial motivation; the second is the relationship between motivation and performance; the third relates to the existence of perverse effects of incentives on motivation, which can take the form of undermining or crowding-out effects. The results are discussed in terms of their implications for economic theory, showing that different mechanisms are at work under intrinsic or prosocial motivation, implying the need for different instruments to promote behaviors and associated performance. In terms of crowding-out effects, there is little evidence to support a perverse effect when incentives are offered before or during performance, whereas the psychological literature provides consolidated validation for the undermining effect. Economics can gain insights from other disciplines by employing their investigative tools and theoretical developments. A feature of particular interest for economics is gamification, that is, the use of game design elements (design of video games and similar games) in non-game contexts