Reconstructing deterministic economic dynamics from volatile time series data

Authored by: Ray Huffaker , Ernst Berg , Maurizio Canavari

The Routledge Handbook of Agricultural Economics

Print publication date:  July  2018
Online publication date:  July  2018

Print ISBN: 9781138654235
eBook ISBN: 9781315623351
Adobe ISBN:


 Download Chapter



Economists conventionally attribute observed volatility in economic time series data to exogenous random shocks that agitate otherwise stable real-world markets and, consequently, model volatility with a variety of linear–stochastic and probabilistic methods. However, some economists have recognized another possible explanation for volatility: markets may be intrinsically unstable, and we might be able to model attending volatility parsimoniously with low-dimensional, nonlinear, deterministic dynamic models without resorting to stochastic inputs. Whether observed volatility is generated by inherently stable or unstable markets has serious policy implications. Will laissez-faire policies suffice to dampen volatility because markets are self-correcting, or are interventionist policies required? This chapter introduces nonlinear time series analysis (NLTS) – a collection of methods developed in mathematical physics to diagnose the source of real-world volatility from observed time series data. Depending on data quality, economists can potentially use NLTS to reconstruct phase-space market dynamics and extract equations of motion from a single price series.

Search for more...
Back to top

Use of cookies on this website

We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more in our Privacy Policy. By continuing to use the site you are agreeing to our use of cookies.