Applied Evolutionary Economics and Complex Systems

Applied Evolutionary Economics and Complex Systems

Edited by John Foster and Werner Hölzl

This book takes up the challenge of developing an empirically based foundation for evolutionary economics built upon complex system theory. The authors argue that modern evolutionary economics is at a crossroads. At a theoretical level, modern evolutionary economics is moving away from the traditional focus of the operation of selection mechanisms and towards concepts of ‘complex adaptive systems’ and self-organisation. On an applied level, new and innovative methods of empirical research are being developed and considered. The contributors take up this challenge and examine aspects of complexity and evolution in applied contexts.

Chapter 3: Random walks and non-linear paths in macroeconomic time series: some evidence and implications

Franco Bevilacqua and Adriaan van Zon

Subjects: economics and finance, evolutionary economics


Franco Bevilacqua and Adriaan van Zon 1. INTRODUCTION The aim of this chapter is to identify the nature of the dynamics of macroeconomic time series. When time series are characterized by zero autocorrelation for all possible leads and lags, the issue of distinguishing between deterministic and stochastic components becomes an impossible task when linear methods are used (Hommes 1998). This impasse arises because linear methods are appropriate to detect regularities in time series like autocorrelations and dominant frequencies (Conover 1971, Oppenheim and Schafer 1989), while fluctuations in real economic time series are generally characterized by zero autocorrelation and no dominant frequency. Economic fluctuations seem really similar to background noise, which does not possess dominant frequencies and each noise impulse is not serially correlated. The spectral analysis of economic fluctuations, seemingly as complex as noise, has led many economists to consider fluctuations as identically independently distributed (i.i.d.) events. As a matter of fact the i.i.d. hypothesis is an obvious necessity for all linear models to describe, at least approximately, the irregularities in the observed data. In the past two kinds of linear economic models based on the i.i.d. hypothesis in the residuals have been presented. In the first model, known as the deterministic trend model, variables evolve as a function in time along a linear trend. In the second model (the stochastic trend model), variables evolve as a function of their forgoing values and a shock shifts the value of the variable from the lagged value (Rappoport and Reichlin 1989). In...

You are not authenticated to view the full text of this chapter or article.

Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.

Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.

Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.

Further information