Marisa Faggini

Chaotic time series analysis in economics: Balance and perspectives

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 economic data. More specifically, our attention will be devoted to reviewing some of these techniques and their application to economic and financial data in order to understand why chaos theory, after a period of growing interest, appears now not to be such an interesting and promising research area.

Visual Recurrence Analysis: Application to Economic Time series

The existing linear and non-linear techniques of time series analysis (Casdagli, 1997), long dominant within applied mathematics, the natural sciences, and economics, are inadequate when considering chaotic phenomena.

Coping with the Complexity of Economics

This book features papers dedicated to the memory of Massimo Salzano, who has been a fervent advocate of the complexity approach. It provides an overview to recent developments in theory and empirical research that view economic systems as complex phenomena.

Analysis of Economic Fluctuations: A Contribution from Chaos Theory

The nature of business cycles is a central and conflicting question in macroeconomics. We would like to stress, however, the importance of chaos, in the context of business cycle theories. In fact, those who believe in i.i.d. disturbances simply state that fluctuations are determined by exogenous factors. Chaos supporters, on the other hand, disagree with a linear world and believe that the source of fluctuations is endogenous to the economic system. The aim of paper is to highlight the power of chaos theory to analyze business cycles.

Complexity in economics : cutting edge research

Applications of Methods and Algorithms of Nonlinear Dynamics in Economics and Finance.- Kaldorian assumptions and endogenous fluctuations in the dynamic fixed-price IS-LM model.- Determining the relationship between Co-Creation and Innovation by Neural Network.- On the fractal characterization of a system for tradings on Eurozone stocks.- Managing uncertainty in complex projects.- On the Concept of Endogenous Volatility.- Chaotic Order - The Economic Relativity.- The Strange Attractor of the Firm.- Interaction-Based approach to Economics and Finance.- Why should Economics give Chaos Theory another chance.- Disequilibrium Trade and the Dynamics of Stock Markets.

Fitness Landscape and Tax Planning: NK Model for Fiscal Federalism

This paper rises from the idea to highlight how traditional models of Fiscal Federalism are not be able to capture adequately the behavioral dynamics of economic systems. We stress the innovative aspects of complexity theory and the premises on which to base the analysis of Fiscal Federalism in this perspective. For this purpose, we consider Fiscal Federalism as a network of economic relationships between different complex adaptive and co-evolving systems, the jurisdictions, linked by strong interdependencies. We will proceed to model a landscape in which co-evolving jurisdictions have to find the optimal path to organize the local tax planning and to optimize their local economy.

A New Perspective for Fiscal Federalism: The NK Model

Abstract: Economic models of fiscal federalism, according to different settings, are generally linear and static, offering unique and deterministic solutions starting with simplifying assumptions. This article stems from the idea of investigating how decision-makers, abandoning their traditional economic models and focusing on innovative components of evolutionary economics instead, can achieve better performance results in organizing and optimizing 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 Stuart Kauffman’s NK model. The relevance of the NK model in the study of economic organizations has been noted in the relevant literature. This literature, however, neglects the problem of co-evolution, which underpins our article.

Innovation Policies: Strategy of Growth in a Complex Perspective

The aim of this chapter is to highlight new understandings of innovation as an interactive process in relation to economic growth. The resulting ideas could be of considerable interest to innovation policy makers. Two impacts are of considerable potential importance. The first relates to the absorption of complex and evolutionary systems dynamics ideas into the study of innovation, and growth. The second relates to the synthesis of complex systems ideas with evolutionary models of innovation, and growth. Considering innovation as a complex multi-level process means that it is not possible to devise the context into independent ways and that it is not enough to provide policymakers with simple solutions, but it should help them formulate and address questions that are appropriate to the evolutionary and complex context within which they operate.

Recurrence Analysis - Method and Applications

The awareness that phenomena (social, natural) are for the most part complex and consequently require more realistic models has led to the development of powerful new concepts and tools to detect, analyse, and understand non-stationarity and apparently random behaviour. Almost all existing linear and nonlinear techniques used for the study of time series presume some kind of stationarity, but the application of such tools to non-stationarity and apparently random time series produces misleading results. Recurrence analysis is an advanced technique for nonlinear data analysis used to identify the general structure, non-stationarity, and hidden recurring elements in a time series. Differently from traditional time series techniques that previously assume the nature of the series, the recurrence analysis can be conceived as a diagnostic tool which provides an exploratory analysis identifying the structure of the series. After a general overview of the epistemological and technical underpinnings for the emergent concepts of complexity and nonlinearity, this paper examines the main features of the technique through theoretical examples and a significant review of the main applications.