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:

10.4324/9781315623351-29

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Abstract

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.

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